Machine Learning Meets Blockchain: A Domain-Oriented Review of Integration Mechanisms, Applications and Open Challenges

Blockchain and machine learning (ML) are combined in systems that must learn from multi-party data and keep a verifiable record of what was used and decided. This scoping-style review of a ranked sample covers seven domains: healthcare, finance, supply chain, energy, smart contracts, transportation and education. We searched Scopus and IEEE Xplore on 15 October 2026, ranked 5195 candidate records, read 345 abstracts in rank order, coded a core corpus of 192 studies and read 38 in full. Federated learning is the most common method (93 of 192 core studies name it), and detection of fraud, intrusions and vulnerabilities is the most common task among the 38. None of the 38 reports a deployment, 24 report a measured ledger figure and two measure ledger energy. Ten use more than one privacy mechanism, and only two report accuracy with and without it. Attack tests are small, and one study tests a Sybil attacker. Studies differ too much to compare, so we do not rank domains. A ledger shows that a record has not changed, not that it was correct when written. The main gaps are realistic evaluation, shared benchmarks and reporting standards, and tested explanations for decisions made inside contracts.