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Food recalls remain essential instruments for protecting public health when unsafe, contaminated, mislabelled, or otherwise non-compliant products enter food supply chains. Yet many recall systems still operate primarily as downstream corrective mechanisms after hazards have already moved through production, distribution, retail, food service, or household channels. This reactive orientation can delay containment, increase exposure, and weaken public confidence in food safety governance. Conventional recall management is increasingly challenged by globalised sourcing, rapid distribution, e-commerce, multi-ingredient products, outsourced processing, fragmented traceability records, and uneven information sharing among actors. Weak risk signals may appear in laboratory data, consumer complaints, inspection findings, supplier records, online reports, or public health surveillance before they are recognised as recall-relevant evidence. When these signals remain disconnected, recall decisions may occur only after risk propagation has already widened. This article proposes a conceptual model for intelligent food recall systems that integrates early detection, risk signal fusion, network propagation mapping, coordinated response, accountability, and communication feedback. The model reframes recall management as a system intelligence problem rather than a narrow compliance, logistics, or product-removal task. It explains how anticipatory analytics and network-aware decision support can strengthen recall scope definition, actor alignment, and public communication. The contribution of the article is a structured conceptual framework for designing recall systems that are more anticipatory, traceable, coordinated, accountable, and adaptive. The model does not report new empirical data; rather, it synthesises food safety, artificial intelligence, supply-chain traceability, risk communication, and crisis-response perspectives into an integrated recall intelligence architecture. It offers a foundation for future empirical testing, digital system design, regulatory innovation, and food-industry implementation.