​Advances in artificial intelligence (AI) now enable continuous collection and interpretation of individual health data, moving healthcare and nutrition beyond one-size-fits-all approaches. Personalized and precision nutrition customizes genetic, metabolic, microbiome, and lifestyle data to tailor dietary recommendations that align with individual needs and health goals. These innovations empower individuals and professionals to make targeted decisions that reflect unique biological, behavioral, and environmental factors. To our knowledge, this is the first systematic review to exclusively focus on the intersection of ontologies with a preliminary analysis of Knowledge Graphs (KGs) in human nutrition. The findings reveal a growing use of AI tools to develop personalized recipes, nutrient intake recommendations, food suggestions, dietary plans, and recommendation systems. However, significant gaps persist in the standardization of methodologies, resource integration, and evaluation criteria, limiting interoperability and scalability for personalized applications. Ontology-based systems dominate knowledge representation, while KGs are being explored for personalization in diet planning and data integration. By bridging computational techniques with nutritional science, this review establishes the foundation for innovative applications in digital health and personalized nutrition, contributing to improved public health outcomes.

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