The convergence of big data analytics and artificial intelligence (AI) is revolutionizing the field of nutrigenomics, a branch of nutritional science focused on the interaction between nutrients and genes. With the increasing availability of genomic, transcriptomic, metabolomic, and dietary data, there is a growing need for advanced computational tools to interpret complex biological patterns and translate them into personalized nutrition strategies. Big data technologies enable the collection, storage, and processing of high-volume, high-variety datasets, while AI algorithms uncover hidden relationships and predictive insights. This chapter explores the integration of AI and big data in nutrigenomics, emphasizing their potential to identify biomarkers, optimize dietary interventions, and predict individual responses to nutrients. The discussion includes applications of machine learning, deep learning, and multi-omics integration, along with their challenges, such as data quality, privacy, and ethical concerns. By bridging computational tools with nutritional sciences, this chapter aims to provide food scientists and technologists with a comprehensive understanding of how data-driven approaches can shape the future of personalized nutrition and public health.