<p>Ensuring food safety and quality has emerged as a critical global priority, driven by rapid growth of the world population and the increasing complexity of modern food production and distribution systems. As supply chains become more globalized and diversified, risks associated with contamination, adulteration, and quality degradation have intensified, posing significant challenges to public health and international trade. Conventional detection and monitoring methods, although scientifically established, often face limitations in efficiency, cost-effectiveness, and adaptability to diverse contamination sources and dynamic production environments. In recent years, the emergence of deep learning as a transformative branch of artificial intelligence has opened new opportunities to address these limitations through automated data analysis and intelligent pattern recognition. This review provides a comprehensive synthesis of advances in artificial intelligence applications for food safety and quality control from 2019 to 2025, covering hazard detection, quality evaluation, and intelligent monitoring for real-time risk prediction and decision support. By systematically analyzing representative methodologies, technical frameworks, and performance outcomes, the review underscores the advantages of artificial intelligence in achieving high-throughput, non-destructive, and precise analytical performance across complex food matrices. It also identifies persistent challenges, including data imbalance, limited interpretability, and environmental variability, which hinder large-scale deployment and regulatory integration. The convergence of artificial intelligence with advanced sensing technologies, big data analytics, and domain expertise is expected to drive the evolution of next-generation intelligent food safety systems, ultimately enabling more transparent, adaptive, and sustainable approaches to postharvest processing, quality management, and global food security.</p>