This poster presents an integrated machine-learning framework for decoding metabolic health from large-scale NMR metabolomics data, combining 1D ¹H NOESY spectra, fast 2D J-resolved experiments, quantified metabolites, and predicted clinical parameters to assess biological age and classify disease states. Using over 30,000 serum samples, the approach achieves highly accurate metabolic age estimation (R = 0.92) and robust multiclass disease classification across nine health categories (accuracy = 0.80, AUC = 0.91–1.00). Analyses of metabolic age distortion reveal disease-associated physiological alterations, while SHAP-based interpretation highlights the relevance of inflammatory and metabolic stability markers. The framework also enables individualized diagnostic reports, illustrating its potential as a scalable, non-invasive tool for precision metabolic phenotyping and personalized health monitoring.