Abstract
<jats:p>Environmental contamination poses persistent risks to public health and food safety, intensified by complex exposure pathways across air, water, soil, and food systems. This chapter examines artificial intelligence–driven monitoring frameworks for detecting, predicting, and managing environmental and foodborne contaminants. Evidence shows that AI-based approaches outperform conventional methods in real-time surveillance, source attribution, and predictive risk assessment. Integration of machine learning, deep learning, IoT sensors, remote sensing, and big data analytics enables precise tracking of hazards. In food systems, AI enhances pesticide residue detection, microbial risk prediction, and supply chain traceability, reducing foodborne disease burden and strengthening governance. AI models also enable scenario analysis, exposure forecasting, and targeted interventions for vulnerable populations. Despite challenges related to data quality, interoperability, and model AI substantially advances environmental risk assessment, preventive strategies, and evidence-based policy</jats:p>