Metabolic syndrome (MetS) affects about 25% of the adult population worldwide and is a primary antecedent to cardiovascular disease and type 2 diabetes. The opportunity of early prediction through predictive modeling allows us to enable timely intervention; however, clinical adoption is stalling despite significant improvement in the algorithms. The present narrative review aims to provide an overview of the fast-evolving field of predictive modeling for early detection of MetS, scanning from basic risk scores, past machine learning models to real world clinical use cases with its current limitations. We reviewed trends in machine learning paradigms, multi-omics integration, wearable technology use, health disparities and other areas of emerging importance to the field by analyzing peer-reviewed literature and offering expert-led commentary on methodological innovations and translational hurdles. While the DL models offer significantly better discriminative ability (∼AUC 0.85-0.95), interpretability becomes an even greater issue. The combination of multiple-omics and longitudinal modeling holds promise for personalized risk assessment. There are critical unmet needs in external validation, algorithmic fairness, and clinical workflow integration. Regulations for the AI-driven diagnostic tools are still in their infancy. Although prediction modeling for MetS has advanced to be technical, successful translation and implementation in clinical contexts should consider interpretability, validation quality, health disparity, and regulatory framework. Potential areas to explore include federated learning models, causal modeling techniques, and implementation of science frameworks.