Title:

A REVIEW OF MACHINE LEARNING APPLICATIONS IN BLOCKCHAIN

Author:

Priyesha Dnyaneshwar Gharat and Dr. Lalit Kumar Khatri

Abstract:

Blockchain and Machine Learning represent two of the most disruptive digital technologies of the 21st century. Blockchain provides decentralization, transparency, immutability, and cryptographic security, while ML enables predictive analytics, pattern recognition, and intelligent automation. The integration of these technologies creates intelligent decentralized ecosystems capable of self-optimization, automated fraud detection, dynamic consensus management, and privacy-preserving analytics. This comprehensive review synthesizes recent advances in ML-enabled blockchain systems, categorizing research into consensus optimization, anomaly detection, smart contract auditing, predictive modeling, scalability enhancement, and privacy-preserving federated learning. The paper critically evaluates architectural integration models, technical challenges, regulatory implications, and future research directions. The findings reveal that while ML significantly enhances blockchain efficiency and security, computational overhead, interpretability issues, and regulatory compliance remain key challenges.

Keyword:

Blockchain, Machine Learning, Deep Learning, Smart Contracts