A Post-Quantum Cryptography and Machine Learning-Driven Framework for Securing Blockchain-Based Supply Chain

Counterfeiting and cybersecurity threats are two major challenges the global supply chain faces and requires
extensive solutions for authenticity, security, and transparency. In some earlier work, we used machine learning in ML
techniques to improve supply chain security by detecting anomalies and fraudulent activities. The emergence of quantum
computing has introduced new weaknesses in existing cryptography techniques, thus requiring the development of anti
quantum solutions. This paper introduces a novel framework that integrates post-quantum cryptography (PQC) and
machine learning (ML)-driven anomaly detection to secure blockchain-based supply chains against new threats PQC Data
and blockchain via quantum-secure encryption -Protection of transactions Does, when Your ML The model analyzes real
time delivery of data for anomaly detection and fraud prevention In addition, a system incorporates IoT Tools for real-time
monitoring, advanced tokenization to traceability, smart contract enforcement automation, and hybrid consent mechanism
ensures easy, robust user friendliness Through medical data analysis, the system demonstrates an ability to reduce
counterfeiting and to improve stakeholder confidence and to establish flexible, future-proof supply chains.
Keywords: Block chain technology, Block chain-Enabled Unique Identification System (BEUIS), counterfeit drugs, public
health, web application, supply chain