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.