Metabolomic profiling predicts future disease in population cohorts, but its value in real-world healthcare remains uncertain. We profiled 250 nuclear magnetic resonance (NMR) biomarkers in 54,933 participants from a diverse integrated US healthcare system linked to longitudinal electronic health records, evaluating nine cardiovascular-kidney-metabolic disease endpoints. Adding NMR to standard-of-care predictors improved discrimination for nearly all endpoints, with the largest gains for type 2 diabetes, chronic kidney disease and metabolic dysfunction-associated steatotic liver disease. Beyond disease onset, NMR improved prediction of disease-specific complications and cross-organ outcomes. Gains persisted across demographic, anthropometric, and genetic-risk strata and were greatest where standard-of-care prediction was weakest. NMR-enhanced models generally preserved calibration, improved high-risk enrichment and increased decision-curve net benefit. Together, these findings support NMR metabolomics as a common approach to risk stratification across the cardiovascular-kidney-metabolic disease continuum and illustrate how NMR-enhanced risk estimates could be integrated with longitudinal clinical information.