Integrating AI in Sustainable Food and Health Systems: Bridging Nutrigenomics, Clinical Care, and Engineering

Artificial Intelligence (AI) is rapidly transforming food systems by offering tools that improve productivity, safety, personalization, and resilience across the value chain. This review synthesizes current evidence on AI applications in agriculture, food processing, personalized nutrition, and supply chain management, and outlines governance and research priorities to ensure that technological gains translate into improved nutrition, equity, and sustainability. This study examined peer-reviewed literature and recent reports to map AI methods, use cases, benefits, and limitations. In agriculture, AI has enhanced precision farming, phenotyping and breeding, and post-harvest handling through sensor-based monitoring, predictive modeling, and automated decision support, leading to improved yields and produce quality. In food safety and processing, computer vision and machine learning have advanced contamination detection, quality grading and process optimization, reducing waste and improving consistency. In personalized nutrition, AI models integrate dietary records, phenotypic indicators and multiomic data to generate individualized recommendations and adaptive interventions that can improve metabolic outcomes and dietary adherence. For supply chain resilience, AI enabled forecasting, traceability and risk assessment support rapid response to disruptions and improve logistical efficiency. Despite demonstrable gains, widespread adoption faces challenges including variable data quality, algorithmic bias, limited transparency, infrastructure gaps, and potential environmental tradeoffs. Equity concerns emerge when resource constrained producers and consumers lack access to data, tools or skills. We propose a framework for responsible AI in food systems that emphasizes standards for data governance and model validation, inclusive design and capacity building, transparent reporting and life cycle assessment to evaluate environmental impacts. Policy levers, public private partnerships and cross disciplinary research are needed to harmonize technological innovation with nutritional and sustainability goals. Finally, we identify priority research areas including scalable validation studies, interoperable data platforms, methods to mitigate bias, and metrics to quantify nutritional and environmental co benefits. By integrating AI with sound governance and evidence based evaluation, the food sector can harness digital advances to support safe, nutritious and sustainable diets at scale. This review offers actionable recommendations for practitioners, researchers and policymakers to guide implementation, monitoring and evaluation of AI interventions that advance food security and public health and equity.