Anju Kanicheril Ambikalekshmi, Poojitha Pushparaj, Elsa Cherian, Rosamma Rajan, Lakshmi Mohan
ABSTRACT This review paper provides a comprehensive overview of how artificial intelligence (AI) is transforming food safety, particularly in detection and surveillance technologies. It highlights AI approaches such as machine learning (ML), deep learning (DL), natural language processing (NLP), and computer vision, emphasizing their role in detecting physical, chemical, and microbial contaminants, ensuring quality control, and predicting outbreaks. This paper also examines real‐world applications and recent case studies while addressing challenges such as data privacy, technical barriers, and regulatory acceptance. Furthermore, it proposes future research directions, including AI–IoT integration, blockchain‐enabled traceability, and quantum computing applications. By synthesizing recent developments (2021–2024), this review aims to guide researchers, policymakers, and industry stakeholders toward leveraging AI for a safer and more resilient food supply chain.