Severe combat trauma triggers long-lasting systemic changes that standard anatomical injury scales may not fully capture, according to an analysis of molecular profiles from 53 combat trauma survivors during rehabilitation compared with 46 uninjured active-duty military controls. Using a multidimensional biomarker panel including IL-33, ST2, TGF-β1, CTGF, galectin-3, heparan sulfate, NT-proBNP, and annexin A5 alongside routine clinical parameters, researchers applied unsupervised clustering and Random Forest machine learning to identify distinct patient endotypes. Results showed patients grouped into molecular phenotypes independent of initial structural injury patterns. While localized structural injuries and associated infections, such as limb amputations, significantly influenced pro-fibrotic and pro-coagulant pathways, the overarching systemic features defining machine-learning endotypes linked to an immuno-metabolic axis. This axis is characterized by elevated IL-33, endothelial glycocalyx shedding, and markers of metabolic, hepatic, and hematological strain—hemoglobin, white blood cells, AST, ALT, and total protein—rather than classical fibrotic markers. Notably, circulating IL-33 levels varied significantly among patients, while its soluble receptor, ST2, remained stable. These findings suggest high-risk trauma patients may experience ongoing, ligand-driven systemic exhaustion. Integrating molecular endotyping into military medicine could improve clinical monitoring and guide targeted rehabilitation strategies.