• R. Ashok Raj

The rapid advancement of distributed systems, blockchain platforms, and quantum computing is fundamentally transforming modern cybersecurity requirements. While blockchain enables decentralized trust and immutable record management, the classical cryptographic foundations of these systems remain vulnerable to quantum-enabled attacks. Simultaneously, Machine Learning (ML) has emerged as a powerful tool for intelligent threat detection, anomaly analysis, ransomware identification, and adaptive intrusion prevention across large-scale digital infrastructures. The review categorizes existing works across five major dimensions: ML-driven cyber-threat intelligence, PQC-based cryptographic modernization, blockchain security enhancement, federated and decentralized learning protection, and integrated ML–PQC defense architectures. Reported findings demonstrate substantial improvements in detection accuracy (frequently exceeding 95–99%), enhanced blockchain throughput under quantum-resilient designs, optimized energy efficiency through adaptive consensus models, and stronger privacy guarantees via decentralized learning and zero-knowledge mechanisms. However, critical challenges remain, including scalability limitations, computational overhead of PQC algorithms, large signature sizes, reliance on simulated environments and integration complexity across heterogeneous platforms, dataset constraints, adversarial ML risks, and insufficient real-world quantum validation. The study analyzes ML-based blockchain threat detection, federated learning enabled privacy preservation, quantum-resistant cryptographic architectures, lightweight PQC deployments for IoT environments, intelligent consensus optimization, decentralized auditing mechanisms, and implementation-level cryptographic vulnerabilities. Sustainability considerations such as energy efficiency and carbon footprint also require deeper evaluation in quantum-safe deployments. Finally, this literature review paper systematically examines recent research focusing on the convergence of Machine Learning–driven security mechanisms and Post-Quantum Cryptographic (PQC) frameworks for building resilient and future-proof distributed ecosystems.

Machine Learning, Artificial Intelligence, Federated Learning, Post-Quantum Cryptography (PQC), Blockchain Security, Quantum-Resistant Consensus, Intrusion Detection Systems, Smart Grid Security, IoT Security, Zero-Knowledge Proofs.

"Machine Learning-Driven Security and Post-Quantum Cryptographic Integration for Secure Blockchain Systems: A Comprehensive Literature Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 3, page no.d565-d578, March-2026, Available :http://www.jetir.org/papers/JETIR2603369.pdf

"Machine Learning-Driven Security and Post-Quantum Cryptographic Integration for Secure Blockchain Systems: A Comprehensive Literature Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 3, page no. ppd565-d578, March-2026, Available at : http://www.jetir.org/papers/JETIR2603369.pdf