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International Journal of Computational and Electronics Aspects in Engineering (IJCEAE)
Volume 7 · Issue 4 · September 2026 · pp. 498–511
Review Article · Peer Reviewed
Received: July 30, 2026 · Accepted: September 16, 2026 · Published: September 27, 2026
DOI: 10.26706/ijceae.7.4.20260815
Open Access · CC BY 4.0

Blockchain and Artificial Intelligence Integration: A Review of Applications, Challenges, and Future Directions

Areej Mahmoud Asaad1,*, Mahmood Hameed Qahtan2, Ahmed Kh. Younis3
1Department of Computer Techniques Engineering, Technical Engineering College for Computer and Artificial Intelligence, Mosul, Northern Technical University, Mosul 41001, Iraq.
2Department of Artificial Intelligence Techniques Engineering, Technical Engineering College for Computer and Artificial Intelligence, Mosul, Northern Technical University, Mosul 41001, Iraq.
3Department of Computer Techniques Engineering, Technical Engineering College for Computer and Artificial Intelligence, Mosul, Northern Technical University, Mosul 41001, Iraq.

*Corresponding author: areej_mahmoud@ntu.edu.iq
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Abstract

Blockchain and AI were developed separately, but nowadays, there is research on the integration of those technologies to benefit from both. First, the blockchain technology gives such possibilities as data provenance, decentralized trust, and tampering resistance; second, the artificial intelligence gives prediction possibilities and algorithms for making decisions. Literature review about the research which has been done in 2026 concerns the combination of blockchain and AI in the following fields: health care, financial industry, logistics, energy sector, smart cities, and decentralized training using AI.

Advantages connected with security, data integrity, privacy, and efficiency, as well as possible barriers associated with scalability, computational costs, quality of data, and regulation are mentioned in the review. The review shows that blockchain may increase security and trust in AI systems, while AI may increase efficiency of blockchain systems. The possible trends include lightweight inference, privacy-preserving machine learning, and AI-based smart contracts security.

Keywords

blockchain artificial intelligence federated learning smart contracts decentralized systems data privacy

References

  1. M. Andoni, V. Robu, D. Flynn, S. Abram, D. Geach, D. Jenkins, P. McCallum, and A. Peacock, "Blockchain technology in the energy sector: A systematic review of challenges and opportunities," Renewable and Sustainable Energy Reviews, vol. 100, pp. 143–174, 2019, doi: 10.1016/j.rser.2018.10.014.
  2. S. Makridakis and K. Christodoulou, "Blockchain: Current challenges and future prospects/applications," Future Internet, vol. 11, no. 12, p. 258, 2019, doi: 10.3390/fi11120258.
  3. Z. Ngoupayou Limbepe, K. Gai, and J. Yu, "Blockchain-based privacy-enhancing federated learning in smart healthcare: A survey," Blockchains, vol. 3, no. 1, p. 1, 2025, doi: 10.3390/blockchains3010001.
  4. G. Tripathi, M. A. Ahad, and G. Casalino, "A comprehensive review of blockchain technology: Underlying principles and historical background with future challenges," Decision Analytics Journal, vol. 9, p. 100344, 2023, doi: 10.1016/j.dajour.2023.100344.
  5. J. Shi and Y. Wang, "Academic exploration of blockchain and AI in financial services," Journal of Electronic Business & Digital Economics, vol. 4, no. 2, pp. 270–282, 2025, doi: 10.1108/JEBDE-08-2024-0023.
  6. T. Handra, N. Lutfiani, A. Aprillia, F. P. Oganda, F. Amelia, and N. Rangi, "Adaptive workflow management with decentralized AI in blockchain based distributed ledger systems," Blockchain Frontier Technology, vol. 5, no. 1, pp. 71–79, 2025, doi: 10.34306/bfront.v5i1.804.
  7. B. Shrimali and H. B. Patel, "Blockchain state-of-the-art: Architecture, use cases, consensus, challenges and opportunities," Journal of King Saud University – Computer and Information Sciences, vol. 34, no. 9, pp. 6793–6807, 2022, doi: 10.1016/j.jksuci.2021.08.005.
  8. I. Shah, W. Dange, N. Nirgule, and D. Ilme, “Next-generation digital voting system,” International Journal of Computational and Electronics Aspects in Engineering, vol. 2, no. 3, pp. 71–75, Jul. 2021, doi: 10.26706/ijceae.2.3.20210606.
  9. A. Alhaidari, B. Palanisamy, and P. Krishnamurthy, “On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation,” in Proc. 7th Conference on Advances in Financial Technologies (AFT 2025), LIPIcs, vol. 354, pp. 35:1–35:27, 2025, doi: 10.4230/LIPIcs.AFT.2025.35.
  10. R. Lavin, X. Liu, H. Mohanty, L. Norman, G. Zaarour, and B. Krishnamachari, “A survey on the applications of zero-knowledge proofs,” arXiv preprint arXiv:2408.00243, 2024, doi: 10.48550/arXiv.2408.00243.
  11. L. T. Thibault, T. Sarry, and A. S. H. Hafid, “Blockchain scaling using rollups: A comprehensive survey,” IEEE Access, vol. 10, pp. 123775–123813, 2022, doi: 10.1109/ACCESS.2022.3200051.
  12. N. Kühl, M. Schemmer, M. Goutier, and G. Satzger, "Artificial intelligence and machine learning," Electronic Markets, vol. 32, no. 4, pp. 2235–2244, 2022, doi: 10.1007/s12525-022-00598-0.
  13. J. Zhang, Y. Che, R. Liu, Z. Wang, and W. Liu, "Deep learning–driven multi-omics analysis: Enhancing cancer diagnostics and therapeutics," Briefings in Bioinformatics, vol. 26, no. 4, p. bbaf440, 2025, doi: 10.1093/bib/bbaf440.
  14. S. Ahmed, M. S. Kaiser, S. Chaki, S. Aloteibi, and M. A. Moni, “Federated learning model with dynamic scoring-based client selection for diabetes diagnosis,” Knowledge-Based Systems, vol. 320, Art. no. 113662, 2025, doi: 10.1016/j.knosys.2025.113662.
  15. D. Bhumichai, C. Smiliotopoulos, R. Benton, G. Kambourakis, and D. Damopoulos, "The convergence of artificial intelligence and blockchain: The state of play and the road ahead," Information, vol. 15, no. 5, p. 268, 2024, doi: 10.3390/info15050268.
  16. M. M. Orabi, O. Emam, and H. Fahmy, "Adapting security and decentralized knowledge enhancement in federated learning using blockchain technology: Literature review," Journal of Big Data, vol. 12, p. 55, 2025, doi: 10.1186/s40537-025-01099-5.
  17. Y. Zuo, "Exploring the synergy: AI enhancing blockchain, blockchain empowering AI, and their convergence across IoT applications and beyond," IEEE Internet of Things Journal, vol. 12, no. 6, pp. 6171–6195, 2025, doi: 10.1109/JIOT.2024.3507746.
  18. O. Kuznetsov, P. Sernani, L. Romeo, E. Frontoni, and A. Mancini, "On the integration of artificial intelligence and blockchain technology: A perspective about security," IEEE Access, vol. 12, pp. 3881–3897, 2024, doi: 10.1109/ACCESS.2023.3349019.
  19. R. K. Veerapaneni and R. Delhibabu, "Trustworthy intelligent rooms: Integrating blockchain, federated learning, and data-centric AI for healthcare 4.0," Frontiers in Digital Health, vol. 8, Art. no. 1758304, 2026, doi: 10.3389/fdgth.2026.1758304.
  20. S. Bulut, “Artificial intelligence research in smart cities: A bibliometric analysis,” Kent Akademisi, vol. 19, no. 2, pp. 1–22, 2026, doi: 10.35674/kent.1669862.
  21. N. Arshadi and T. P. Dombrowski, "Applications and management of blockchain technologies in financial services," Journal of Risk and Financial Management, vol. 19, no. 3, p. 224, 2026, doi: 10.3390/jrfm19030224.
  22. M. Iqbal, Z. Zahraini, H. Ilmi, M. Mutasar, P. Naila, and E. Marra, "Mitigating religious radicalism and polarization through the integration of artificial intelligence (AI), internet of things (IoT), blockchain and cognitive science," AMPLITUDO: Journal of Science and Technology Innovation, vol. 5, no. 1, pp. 148–157, 2026, doi: 10.56566/amplitudo.v5i1.551.
  23. D. Upadhyay, P. Garg, and N. Babbar, "Blockchain and IoT based smart contract framework for efficient and secure product life management," Discover Internet of Things, vol. 6, p. 25, 2026, doi: 10.1007/s43926-025-00274-7.
  24. B. S. Liya, H. Kumar, M. M. M. Hassan, P. Nisha, M. G. Aush, and D. Anand, "Energy efficient cyber-physical control of renewable microgrids using edge-AI enabled IoT and secure blockchain coordination," Scientific Reports, vol. 16, Art. no. 20194, 2026, doi: 10.1038/s41598-026-50169-y.
  25. R. Bai, "Integration of blockchain and AI knowledge management for contamination risk prediction in food logistics," Engineering Reports, vol. 8, Art. no. e70944, 2026, doi: 10.1002/eng2.70944.
  26. R. Pal, "Decentralized market access for farmers using blockchain-AI tools," Scientific Journal of Artificial Intelligence and Blockchain Technologies, vol. 3, no. 3, pp. 17–24, Jul.–Sep. 2026.
  27. M. Alamri, N. Tariq, M. Humayun, and M. Alshammeri, “Energy-efficient threat detection in IoT healthcare using AI and blockchain-enhanced fog–cloud architecture,” Cluster Computing, vol. 29, Art. no. 108, 2026, doi: 10.1007/s10586-025-05932-7.
  28. A. W. Khan, A. Muhammad, Farhan, A. Salam, A. K. K. Baig, M. Tahir, and N. H. Ansari, “An AI-driven blockchain-based cybersecurity framework for secure cloud computing environments,” Spectrum of Engineering Sciences, vol. 4, no. 6, pp. 1745–1762, 2026, doi: 10.5281/zenodo.20728226.
  29. N. Gafsi, A. Hamdouni, and A. Smaoui, "Digital technologies and sustainable development: Evidence from FinTech, AI, and blockchain adoption in G20 economies," Sustainability, vol. 18, no. 5, Art. no. 2484, 2026, doi: 10.3390/su18052484.
  30. O. O. Adebiyi, A. D. Popoola, P. D. Adeyinka, A. T. Arigbabu, and O. O. Olateju, “Blockchain-integrated AI systems for enhancing trust and transparency in cross-border finance,” Asian Journal of Research in Computer Science, vol. 19, no. 1, pp. 271–291, 2026, Art. no. AJRCOS.151919, doi: 10.9734/ajrcos/2026/v19i1818.
  31. G. Mishra and Y. Mishra, “An adaptive neuro-fuzzy blockchain–AI framework for secure and intelligent FinTech transactions,” Scientific Reports, 2026, Art. in press, doi: 10.1038/s41598-026-66643-6.
  32. R. Shinde, S. Patil, K. Kotecha, and S. Mishra, "Ensuring the integrity of AI models: A blockchain-based approach for protecting medical imaging training data," Scientific Reports, vol. 16, no. 1, Art. no. 13989, 2026, doi: 10.1038/s41598-026-44040-3.
  33. A. O. Stephen, C. Liu, and G. Xin, “A smart AI–IoT–blockchain framework for sustainable coal gangue waste systems and circular resource recovery,” Cleaner Waste Systems, vol. 14, Art. no. 100497, 2026, doi: 10.1016/j.clwas.2026.100497.
  34. J. Singh, S. Bharany, S. Rani, A. U. Rehman, B. M. Taye, and R. Pant, "A systematic review of blockchain, AI, and cloud integration for secure digital ecosystems," International Journal of Networked and Distributed Computing, vol. 13, p. 28, 2025, doi: 10.1007/s44227-025-00072-1.
  35. A. I. Kathia, P. Tasca, Y. Erinle, N. Vadgama, A. Satybaldy, and M. A. Javarone, “SoK: A systematic bidirectional literature review of AI & DLT convergence,” arXiv preprint arXiv:2605.10515, 2026, doi: 10.48550/arXiv.2605.10515.
  36. K. D. Pandl, S. Thiebes, M. Schmidt-Kraepelin, and A. Sunyaev, “On the convergence of artificial intelligence and distributed ledger technology: A scoping review and future research agenda,” IEEE Access, vol. 8, pp. 57075–57095, 2020, doi: 10.1109/ACCESS.2020.2981447.
  37. T. Bhardwaj and K. Sumangali, "An explainable federated blockchain framework with privacy-preserving AI optimization for securing healthcare data," Scientific Reports, vol. 15, Art. no. 21799, 2025, doi: 10.1038/s41598-025-04083-4.
  38. R. Shinde, S. Patil, K. Kotecha, V. Potdar, G. Selvachandran, and A. Abraham, “Securing AI-based healthcare systems using blockchain technology: A state-of-the-art systematic literature review and future research directions,” Engineering Technology & Engineering, 2023, doi: 10.1002/ett.4884.
  39. V. Punniyamoorthy, A. G. Parthi, M. Palanigounder, R. K. Kodali, B. Kumar, and K. Kannan, “A privacy-preserving cloud architecture for distributed machine learning at scale,” arXiv preprint arXiv:2512.10341, 2025.
  40. A. M. Al Shareef, S. Seçkiner, B. Eid, and H. Abumeteir, "Integration of blockchain with artificial intelligence technologies in the energy sector: A systematic review," Frontiers in Energy Research, vol. 12, Art. no. 1377950, 2024, doi: 10.3389/fenrg.2024.1377950.
  41. "Augmented finance for climate action: A systematic review of AI, IoT, and blockchain applications in sustainable finance," International Journal of Financial Studies, vol. 14, no. 4, Art. no. 91, 2026, doi: 10.3390/ijfs14040091.
  42. M. R. Sebti, U. McCarthy, A. Ktenioudaki, M. Russo, and M. Merenda, "Traceability and anti-counterfeiting in agri-food supply chains: A review of RFID, IoT, blockchain, and AI technologies," Sensors, vol. 26, no. 5, Art. no. 1685, 2026, doi: 10.3390/s26051685.
  43. S. B. Dhal and D. Kar, "Leveraging artificial intelligence and advanced food processing techniques for enhanced food safety, quality, and security: A comprehensive review," Discover Applied Sciences, vol. 7, no. 1, Art. no. 75, 2025, doi: 10.1007/s42452-025-06472-w.
  44. O. O. Egunjobi, A. Gomes, C. N. Egwim, and H. Morais, "A systematic review of blockchain for energy applications," e-Prime – Advances in Electrical Engineering, Electronics and Energy, vol. 9, Art. no. 100751, 2024, doi: 10.1016/j.prime.2024.100751.
  45. H. Majidi, M. M. Hayati, C. Breyer, B. Mohammadi-ivatloo, S. Honkapuro, H. Karjunen, P. Laaksonen, and V. Sihvonen, "Overview of energy modeling requirements and tools for future smart energy systems," Renewable and Sustainable Energy Reviews, vol. 212, Art. no. 115367, 2025, doi: 10.1016/j.rser.2025.115367.
  46. K. Christidis and M. Devetsikiotis, "Blockchains and smart contracts for the internet of things," IEEE Access, vol. 4, pp. 2292–2303, 2016, doi: 10.1109/ACCESS.2016.2566339.
  47. I. Lukić et al., "Advancing smart city sustainability through artificial intelligence, digital twin and blockchain solutions," Technologies, vol. 13, no. 7, Art. no. 300, 2025, doi: 10.3390/technologies13070300.
  48. A. Alotaibi, H. Aldawghan, and A. Aljughaiman, "A review of the authentication techniques for internet of things devices in smart cities: Opportunities, challenges, and future directions," Sensors, vol. 25, no. 6, Art. no. 1649, 2025, doi: 10.3390/s25061649.
  49. A. Denis, A. Thomas, W. Robert, A. Samuel, S. P. Kabiito, Z. Morish, M. Sallam, G. Ali, and M. M. Mijwil, "A survey on artificial intelligence and blockchain applications in cybersecurity for smart cities," SHIFRA, vol. 2025, pp. 1–45, 2025, doi: 10.70470/SHIFRA/2025/001.
  50. Y. Y. Ghadi, T. Mazhar, T. Shahzad, I. H. Jaghdam, S. Khan, M. A. Khan, and H. Hamam, "A hybrid AI-Blockchain security framework for smart grids," Scientific Reports, vol. 15, no. 1, Art. no. 20882, 2025, doi: 10.1038/s41598-025-05257-w.
  51. A. M. Shamsan Saleh, "Blockchain for secure and decentralized artificial intelligence in cybersecurity: A comprehensive review," Blockchain: Research and Applications, 2024, doi: 10.1016/j.bcra.2024.100193.
  52. X. Hui and C. E. Tucker, "Decentralization, blockchain, artificial intelligence (AI): Challenges and opportunities," Journal of Product Innovation Management, vol. 42, no. 5, pp. 947–957, 2025, doi: 10.1111/jpim.12800.
  53. G. Mandinyenya and V. Malele, "Synthesizing the future of AI-blockchain integration: A pathway for adaptive, ethical, and efficiency," Latin-American Journal of Computing, vol. 13, no. 1, pp. 23–33, 2026, doi: 10.33333/lajc.vol13n1.02.
  54. G. Caldarelli, "Can artificial intelligence solve the blockchain oracle problem? Unpacking the challenges and possibilities," Frontiers in Blockchain, vol. 8, Art. no. 1682623, 2025, doi: 10.3389/fbloc.2025.1682623.
  55. M. F. Mahdi, “Enhancing cloud security through artificial intelligence: Detecting advanced cyber attacks and analyzing anomalous patterns,” International Journal of Computational and Electronics Aspects in Engineering, vol. 6, no. 3, pp. 108–120, Jun. 2025, doi: 10.26706/ijceae.6.3.20250601.
  56. Y. M. Abdal, “AI-driven privacy shield: A secure and privacy-preserving federated learning framework,” International Journal of Computational and Electronics Aspects in Engineering, vol. 6, no. 3, pp. 180–189, Jul. 2025, doi: 10.26706/ijceae.6.3.20250608.
  57. X. Gu, F. Sabrina, Z. Fan, and S. Sohail, "A review of privacy enhancement methods for federated learning in healthcare systems," International Journal of Environmental Research and Public Health, vol. 20, no. 15, Art. no. 6539, 2023, doi: 10.3390/ijerph20156539.
  58. M. Q. Mohammed and Z. A. Jaaz, “A comprehensive review of predictive vulnerability prioritization using AI,” International Journal of Computational and Electronics Aspects in Engineering, vol. 7, no. 1, pp. 27–44, Feb. 2026, doi: 10.26706/ijceae.7.1.20260103.
  59. M. Ozdag, “AI-driven vulnerability analysis in smart contracts: Trends, challenges and future directions,” International Journal of Artificial Intelligence and Applications (IJAIA), vol. 16, no. 3, pp. 65–82, May 2025, doi: 10.5121/ijaia.2025.16305.
  60. N. Choi and H. Kim, "Technological convergence of blockchain and artificial intelligence: A review and challenges," Electronics, vol. 14, no. 1, Art. no. 84, 2024, doi: 10.3390/electronics14010084.
  61. W. Ibrar, D. Mahmood, A. S. Al-Shamayleh, G. Ahmed, S. Z. Alharthi, and A. Akhunzada, "Generative AI: A double-edged sword in the cyber threat landscape," Artificial Intelligence Review, vol. 58, no. 9, Art. no. 285, 2025, doi: 10.1007/s10462-025-11285-9.
  62. H. Yuan, S. Fei, and Z. Yan, "Technologies of blockchain interoperability: A survey," Digital Communications and Networks, vol. 11, no. 1, pp. 210–224, 2025, doi: 10.1016/j.dcan.2023.07.008.
  63. Y. A. Turky, “Transferring heterogeneous database data to relational systems using HTTP and JSON,” International Journal of Computational and Electronics Aspects in Engineering, vol. 7, no. 1, pp. 1–11, Feb. 2026, doi: 10.26706/ijceae.7.1.20260101.
  64. D. Tortola, A. Lisi, P. Mori, and L. Ricci, "Tethering Layer 2 solutions to the blockchain: A survey on proving schemes," Computer Communications, vol. 225, pp. 289–310, 2024, doi: 10.1016/j.comcom.2024.07.017.
  65. M. S. Al Jasem, T. De Clark, and A. K. Shrestha, "Toward decentralized intelligence: A systematic literature review of blockchain-enabled AI systems," Information, vol. 16, no. 9, Art. no. 765, 2025, doi: 10.3390/info16090765.
  66. E. Lui, R. Sun, V. Shah, X. Xiong, J. Sun, D. Crapis, W. Knottenbelt, and Z. Wang, “SoK: Blockchain-Based Decentralized AI (DeAI),” in Proc. IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 2026, doi: 10.48550/arXiv.2411.17461.
  67. S. Alsunaidi, H. Aljamaan, and M. Hammoudeh, "Leveraging machine learning models to improve smart contract security: A survey of vulnerabilities and detection methods," ACM Computing Surveys, vol. 58, no. 6, Art. no. 151, pp. 1–37, 2025, doi: 10.1145/3772367.
  68. R. Asif, S. R. Hassan, and G. Parr, "Integrating a blockchain-based governance framework for responsible AI," Future Internet, vol. 15, no. 3, Art. no. 97, 2023, doi: 10.3390/fi15030097.
  69. D. Kaushik, P. Gulia, N. S. Gill, M. Yahya, P. K. Shukla, and J. Shreyas, "Synergizing blockchain and AI to fortify IoT security: A comprehensive review," Artificial Intelligence Review, vol. 59, Art. no. 41, 2026, doi: 10.1007/s10462-025-11434-0.
  70. R. Belchior, A. Vasconcelos, S. Guerreiro, and M. Correia, “A Survey on Blockchain Interoperability: Past, Present, and Future Trends,” ACM Computing Surveys, vol. 54, no. 8, Art. no. 168, 2022, doi: 10.1145/3471140.