Convergence of Blockchain and Machine Learning for Intelligent Supply Chain Management: A Systematic Analysis of Synergies, Applications, and Emerging Trends

This extensive review analyzes 255 peer-reviewed articles (2020-2025) from the Scopus database to investigate the synergistic integration of blockchain and machine learning (ML) technologies in supply chain management (SCM). Our systematic review highlights five convergence themes at the core: (1) Immutable Traceability - blockchain-based decentralized ledgers ensure product path visibility and ML identifies supply chain data anomalies; (2) Intelligent Forecasting - ML-based algorithms drive demand forecasting and inventory optimization via blockchain-verified historical information; (3) Automated Compliance - smart contracts enforce business rules verified by ML-tested processes; (4) Sustainable Operations - blockchain records carbon footprint and ML optimizes resource usage and waste minimization; and (5) Cyber-Resilience - blockchain-based encryption protects IoT networks with ML-driven threat discovery. Industry-specific findings uncover revolutionary effects Agri-food supply chains achieve 20-30% waste reduction with blockchain origin tracking and ML spoilage forecasting, Pharmaceutical logistics accomplish 99.7% counterfeiting prevention through blockchain provenance tracking and ML pattern detection, Manufacturing demonstrates 25% maintenance cost savings through blockchain-component histories and ML failure prediction Implementation challenges are measured across the literature: scalability constraints impact 68% of real-time applications, integration costs stand at $2.4M on average for businesses, and skill gaps affect 82% of adoption efforts. Blockchain structures that are quantum-resistant, generative AI for supply chain planning, and standards across industries are among the priorities for future research. SCM systems that are transparent, self-optimizing, and robust in line with Industry 5.0 are made possible by this synergy.