Mechanism-driven artificial intelligence for food cold-chain control and governance

Background Food cold chains are critical infrastructures for safeguarding perishable foods, yet prevailing management strategies remain largely rule-based, relying on sparse environmental monitoring, conservative safety margins and endpoint inspection. Such approaches insufficiently capture product-level micro-environment variability and deterioration kinetics, resulting in recurrent temperature abuse, avoidable food loss, excessive energy consumption and elevated greenhouse-gas emissions. Scope and approach This review advances a mechanism-driven perspective on artificial intelligence (AI) in food cold-chain systems. Rather than treating AI as a generic optimisation tool, we examine how near-product, multimodal sensing can be coupled with kinetics-informed and physics-aware models to infer remaining shelf life, safety margins and quality risk in real time. We synthesise evidence on how these mechanism-linked outputs inform dynamic process control, quality-based logistics allocation, first-expired–first-out implementation and energy-aware operation. Particular emphasis is placed on model interpretability, uncertainty quantification, external validation and clearly defined applicability domains as prerequisites for trustworthy deployment. Key findings and conclusions Across reported studies, AI demonstrates tangible value only when tightly anchored to measurable micro-environments and explicit deterioration mechanisms, and when embedded within governed decision loops aligned with formal food-safety management systems. We distil core design principles for mechanism-driven cold-chain intelligence: robust sensing and calibration; physics-informed feature construction; uncertainty-aware modelling; transparent validation across products and scenarios; and controlled transferability. Under these conditions, AI-enabled systems can transition cold chains from static compliance frameworks to dynamic, risk-based governance structures, with realistic potential to reduce avoidable losses, optimise energy demand and lower emissions while enhancing transparency and accountability in cold-chain performance.