<p>Driven by the escalating complexity of global food supply chains and the relentless upgrade in consumption quality, traditional detection paradigms—reliant on manual sensory evaluation and offline physicochemical analysis—struggle to meet rigorous demands for efficiency and objectivity. Machine vision technology, leveraging non-invasive sensing, millisecond-level real-time response, and multi-dimensional information quantification, is reshaping the architecture of intelligent food safety detection. This paper provides a panoramic review of this evolution. It first elucidates the operational mechanisms of high-precision imaging systems and cutting-edge deep learning algorithms, establishing a technical pathway from perception to decision-making. The study critically analyzes application efficacy in four pivotal scenarios: the non-destructive evaluation of appearance phenotypes and freshness, precise discrimination of adulterants in complex matrices, sensitive tracing of trace contaminants, and online removal of foreign bodies. Furthermore, the article deconstructs the deep-seated barriers currently confronting machine vision technology, specifically focusing on the bottlenecks of model generalization and robustness in dynamic environments, the computational complexity of semantic alignment for multi-modal heterogeneous data, and the dependency dilemma regarding large-scale annotated data. Addressing these, prospective development paths are outlined, including few-shot learning strategies, lightweight model deployment on edge computing, and cross-modal knowledge graphs. By systematically synthesizing frontier achievements and extracting theoretical insights, this work aims to facilitate the digital transformation and intelligent escalation of the next-generation AI-empowered food safety defense system.</p>