A Privacy-Preserving AI-Blockchain Framework for End-to-End Supply Chain Traceability

Global supply chains deal with large volumes of essential products, and failure to track them back, whether in fake ingredients and slow recall, or faked records of certification, represents a huge challenge to the finances, policies, and even personal safety. The inability to provide real time checks between distributed stakeholders is characterized by fragmented systems, poor interoperability, and a failure to ensure scan-free audit trails on scale. The available strategies seek to fill these gaps but fail in critical aspects. The centralized ERP/MES systems do not enhance the entire visibility but introduce one-point systems of failure and poor data provenance. Public blockchains are not only immutable, but also have the disadvantage of having high transaction costs, high response time, and no data privacy. There is a better permission control, but there is no smooth fusion of the heterogeneous networks by Consortium blockchains. In the meantime, separate AI-powered risk detection systems are able to anticipate abnormalities, but are not incorporated into the execution layer to promptly institute remedial measures into the supply chain process. This paper proposes a privacy-preserving AI-integrated Layer-2 blockchain traceability framework that leverages Layer-2 rollups for scalable, low-cost event validation and AI-powered smart contracts for autonomous compliance and anomaly response. The design integrates IPFS-based off-chain storage, cross-chain interoperability, and decentralized identity verification (DID) to ensure privacy, scalability, and trust. Results demonstrate a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&gt;99 \%$</tex> reduction in transaction cost and sub-minute counterfeit detection, establishing a scalable model for global, high-value supply chains. Smart contracts that use AI carry out compliance through automatic performance in case of risk identification. Constant retraining pipelines and sharding-based scaling also ensure more flexibility of the system and its ability to fasten over the long term. Product traceability is brought out as scalable, auditable and privacy intended with this integrated design. It shows an innovative architecture that minimizes verification latency, enhances detection of frauds and does not have single point of failure. Combining real-time risk analytics with fully automated, completely independent enforcement, the proposed framework will provide end-to-end visibility needed in global, high-value, and safety-sensitive supply chains.

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