Artificial Intelligence and Machine Learning: Shaping the Future of Food Safety, Quality Control, Traceability Systems, and Nutrition

As food supply chains grow increasingly complex and consumer demands evolve, AI and ML have emerged as vital technologies for enhancing food safety, quality, traceability, and nutrition. Despite their potential, practical applications require further exploration to unlock their benefits fully. This review provides a modern analysis of the advancements of AI and ML in food safety and quality control, focusing on areas such as microbiological detection, contamination prediction, traceability, and real-time monitoring across the food supply chain. The review examines how AI and ML can enhance food safety regulations by leveraging predictive modelling, risk assessment, and decision-support systems to identify and mitigate hazards proactively. It also demonstrates how AI and ML may enhance nutritional assessment and individualized dietary interventions using predictive modelling, nutrient profiling, and clinical decision support. Integrating AI and ML with food safety and nutrition methodologies enhances conventional risk management strategies by identifying issues, forecasting potential problems, and improving monitoring efficiency. These advances enhance food quality, reduce waste, and foster customer trust. Challenges remain, including insufficient data, the need for model interpretability, and concerns over regulatory compliance. However, AI and ML are constantly improving, offering innovative methods to enhance the intelligence and resilience of food safety systems.