Artificial Intelligence in Obesity Prevention

Background/Objectives: Obesity is a complex disorder that causes further health issues linked to several chronic diseases, such as cancer, diabetes, metabolic syndrome, and cardiovascular diseases; thus, it is critical to identify and diagnose obesity as soon as possible. Traditional methods, such as anthropometric measures, were popular, although recent advances in artificial intelligence (AI) offer new opportunities for prediction models; as a result, AI has become an essential tool in obesity research. This study provides a comprehensive analysis of the research on the impact of AI on obesity prevention. Methods: In this study, the researchers performed a scoping study using AI to assess and predict obesity in PubMed, Scopus, Web of Science, and Google Scholar from February 2009 to July 2025. The researchers compiled and arranged the employed AI approaches to find connections, patterns, and trends that could guide further research and the application of machine learning algorithms for advanced data analytics. Results: Clinical professionals in obesity medicine may find chatbots valuable as a source of clinical and scientific knowledge, and for creating standard operating procedures, policies, and procedures. According to the findings, AI models can be used to identify clinically significant patterns of obesity or the connections between specific factors and weight outcomes. Moreover, the application of deep learning and machine learning approaches, such as logistic regression, decision trees, and artificial neural networks, appears to have yielded new insight into data, particularly in terms of obesity prediction. Conclusions: This work aims to contribute to a better understanding of obesity detection. While more studies are needed, AI offers solutions to modern challenges in obesity prediction.