Artificial intelligence for food safety: From predictive models to real-world safeguards

Food safety is no longer just a lab issue; it’s a real-world challenge that affects everyone from farmers and vendors to regulators and consumers. With rising concerns about adulteration, spoilage, and contamination in everyday items like milk, oils, fruits, and ready-to-eat meals, traditional testing methods often fall short; they’re too slow, too expensive, and not designed for real-time action. This review explores how artificial intelligence (AI) and machine learning (ML) are stepping in as game-changers. We highlight real case studies where AI models, combined with tools like spectroscopy, smart sensors, and computer vision, are detecting food fraud and spoilage quickly and accurately. Beyond the technology, we also discuss challenges like data gaps, model trust, and affordability in rural areas, while offering forward-looking solutions like federated learning and low-cost AI devices. This review will be especially valuable for food scientists, quality assurance professionals, tech developers, policy-makers, and startups looking to build safer, smarter food systems. It’s a practical guide for turning AI innovation into real-world food safety solutions. • Artificial Intelligence and Machine Learning enable rapid, accurate, and real-time detection of food adulteration and spoilage, addressing the limitations of traditional lab-based testing methods. • This review showcases the synergy of AI with spectroscopy, smart sensors, and computer vision, enhancing non-invasive analysis of food quality across various categories like milk, oils, and fruits. • Real-world applications are highlighted, demonstrating how AI-driven solutions are already being used in detecting food fraud and contamination with high precision and speed. • Critical barriers such as data gaps, lack of model interpretability , and affordability in rural areas are discussed, underlining the need for trustworthy and inclusive AI models are discussed. • This review explores emerging solutions like federated learning, edge AI, and low-cost diagnostic tools , providing a roadmap for building smarter, scalable, and equitable food safety systems.