Blockchain–AI Hybrid Models for Supply Chain Security: A Secondary Data Synthesis

Abstract

Supply chains all over the world become more computerized, which subjects them to cyberattacks, fraud, counterfeiting, and data manipulations. Distributed and tamper-evident ledgers are available through blockchain, and predictive analytics, anomaly recognition, and intelligent decision-making is provided by Artificial Intelligence (AI). Hybrid models through the combination of such technologies provide secure, transparent, and adaptive supply chains. The current paper is a synthesis of secondary data (20192025) of scholarly journals, industry reports, and international bodies in order to analyze the value of blockchain-AI hybrid systems in improving the security of the supply chain. We overview the use cases in manufacturing, logistics, pharmaceuticals, and food industries and extract the major areas of integration: blockchain to be more data integrity and provenance, and AI to be more analytics and forecast, risk detection. We introduce a comparative table of blockchain-only, AI-only and hybrid models, conceptual hybrid architecture figure, flow diagram of information flow. The results show that the hybrid systems enhance resilience, detecting fraud, and traceability through the connection between unaltered records and adaptive intelligence. Scalability, interoperability, governance and data quality continue to be problematic. The way forward in work should be the standardization of interfaces, guarantee privacy, and create cross-sector models of reliable hybrid supply chain.