Application of Artificial Intelligence in the Diagnosis, Prediction, and Management of Metabolic Syndrome: A Systematic Review

Background and Aims Metabolic syndrome (MetS) is associated with increased risks of cardiovascular disease and type 2 diabetes, with recent global prevalence estimates of approximately 28%–31% in adults. Artificial intelligence (AI) and machine learning offer potential to improve risk stratification beyond conventional statistical methods. This systematic review aims to synthesize evidence on AI applications for the diagnosis and prediction of MetS, while assessing the limited evidence on extensions for prevention and management. Methods Following PRISMA and SAMPL guidelines, a systematic literature search was conducted in 2025 in PubMed, Web of Science, Scopus, and Google Scholar without date restrictions. Search terms included “artificial intelligence,” “machine learning,” “metabolic syndrome,” “diagnosis,” “prediction,” “prevention,” and “management.” Only English‐language original studies were included. Titles/abstracts and full texts were screened independently by two reviewers. Extracted data comprised study characteristics, sample sizes, AI algorithms, feature types, performance metrics, and key findings. Results Of 1178 identified records, 64 full‐text articles were assessed for eligibility; 12 studies ( n = 12/64) met inclusion criteria. The majority addressed diagnostic or predictive tasks using algorithms such as support vector machines (SVMs), artificial neural networks (ANNs), k‐nearest neighbors (KNNs), and random forests (RFs). Most models utilized non‐invasive features and reported area under the curve (AUC) values ranging from 0.82 to 0.99. Evidence for long‐term management or behavioral prevention was present in only a minority of studies ( n = 3/12) and remained largely exploratory. Across the reviewed literature, formal reporting of uncertainty measures (e.g., 95% confidence intervals) and model explainability techniques (e.g., SHAP, permutation importance) was consistently absent. Conclusion AI demonstrates promise for non‐invasive diagnosis and risk prediction of MetS. However, applications in prevention and personalized management remain underdeveloped. Realizing the full clinical potential of AI will require rigorous external validation, enhanced model explainability, and educational promotion to bridge the current implementation gap.