Background: Early identification of metabolic syndrome (MetS) in young adults is important to preventing future cardiovascular and metabolic diseases. South Asians are particularly vulnerable due to disproportionate visceral adiposity at lower body mass indices, this phenomenon making traditional BMI-centric screening insufficient. Methods: A cross-sectional study among Indian young adults assessed regional adiposity. Logistic regression identified independent adiposity predictors, and optimal cut-offs were established via receiver operating characteristic (ROC) curve analysis and the Youden Index. A machine learning–based risk score model was developed using significant predictors. Results: BF%, TAF, IAAT, and SCAT demonstrated strong associations with MetS components (p<0.001). Optimal cut-offs were derived (BF%: 31.22%, TAF: 162.03 cm, IAAT: 110.28 cm, SCAT: 120.34 cm), each showing high discriminatory power (AUC >0.91). The final risk score model achieved an AUC of 0.946, offering excellent predictive ability for early MetS detection. Conclusion: Regional adiposity phenotyping combined with machine learning–based risk prediction enables early, precise identification of MetS risk among young adults.