<p>Computer vision technologies have emerged as powerful tools for advancing food quality and safety assurance by enabling non-invasive, automated inspection processes. In recent years, there has been a significant surge in the adoption of computer vision systems (CVSs), often integrated with artificial intelligence (AI) and deep learning (DL), to improve accuracy, speed, and consistency in evaluating food products. Despite growing interest, the existing literature remains fragmented, with limited cross-domain analysis and a lack of standardized frameworks for industrial implementation. This systematic review addresses the gap by analyzing multiple peer-reviewed studies and identifying key application areas, model architectures, and evaluation metrics across various segments of the food sector, including fruits, vegetables, meat, grains, and dairy. The findings highlight that CV-based systems outperform traditional methods in objectivity, efficiency, and scalability, particularly when combined with AI approaches. The review concludes by identifying the need for standardized datasets, real-time multi-modal systems, the integration of CV with robotics and IoT for next-generation smart food processing, and future research directions.</p>