Modern supply chains are complex networks susceptible to numerous vulnerabilities, including fraud, counterfeiting, inefficiencies, and lack of transparency. This paper proposes an integrated system leveraging blockchain technology for enhanced security and traceability, coupled with an Artificial Intelligence (AI) and Machine Learning (ML) based anomaly detection system implemented in Python. Blockchain provides an immutable and transparent ledger for recording transactions and tracking goods, thereby fostering trust among participants. The AI/ML anomaly detection system analyzes data from the supply chain (including blockchain records and sensor data) to identify unusual patterns, potential disruptions, or fraudulent activities in real-time. This synergistic approach aims to create a more resilient, secure, and efficient supply chain ecosystem. The paper details the architecture of this integrated system, discusses the roles of blockchain and AI/ML components, explores conceptual implementation aspects, and highlights the potential benefits and challenges.