With the role of artificial intelligence (AI) in precision nutrition rapidly expanding, a scoping review on recent studies and potential future directions is demanded. This scoping review examines: 1) the current landscape, including publication venues, targeted diseases, AI applications, methods, evaluation metrics, and considerations of minority and cultural factors
2) common patterns in AI-driven precision nutrition studies
and 3) gaps, challenges, and future research directions. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews process, we extracted 198 articles from major databases with search keywords in 3 categories: precision nutrition keywords, AI keywords, and natural language processing keywords. The extracted literature reveals a surge in AI-driven precision nutrition research, with ∼75% (n = 148) published since 2020. It also showcases a diverse publication landscape, with these studies predominantly focusing on diet-related diseases, such as diabetes and cardiovascular conditions, while emphasizing health optimization, disease prevention, and management. We highlight diverse datasets and critically discuss methodologies and evaluation metrics to guide future studies. Importantly, we underscore the significance of minority and cultural aspects in enhancing health technologies and advancing equity. Future research should deepen the integration of these factors to fully harness AI's potential in precision nutrition.