Targeting the three major obstacles-information fragmentation, delayed risk response, and ambiguous accountability-in food cold chain safety governance, this study constructs an integrated cold chain safety traceability and monitoring system centered on artificial intelligence technology and incorporating the Internet of Things and blockchain. On the basis of a comprehensive risk analysis of the entire food cold chain process, and drawing on theoretical frameworks including supply chain resilience theory, information asymmetry theory, HACCP, and ISO 22000 food safety standards, this study designs a system architecture organized around five functional modules: multi-source data collection and traceability, dynamic food quality assessment, risk prediction and early warning, intelligent scheduling optimization, and blockchain-based trusted evidence storage. This study then proposes a collaborative operational architecture encompassing perception, assessment, prediction, decision-making, and evidence storage, which enables multi-source data fusion, dynamic quality evaluation, and risk warning. This advances the transformation of cold chain safety governance from a reactive post-incident accountability model to a proactive prevention and control model of pre-incident warning and in-process monitoring, while clarifying the functional positioning of artificial intelligence technology in cold chain safety governance.