Abstract Sepsis is a major cause of mortality among hospitalized patients worldwide. Shorter time to administration of broad‐spectrum antibiotics is associated with improved outcomes, but early recognition of sepsis remains a major challenge. In a two‐center cohort study with prospective sample collection from 1400 adult patients in emergency departments suspected of sepsis, we sought to determine the diagnostic and prognostic capabilities of a machine‐learning algorithm based on clinical data and a set of uncommonly measured biomarkers. Specifically, we demonstrate that a machine‐learning model developed using this dataset outputs a score with not only diagnostic capability but also prognostic power with respect to hospital length of stay (LOS), 30‐day mortality, and 3‐day inpatient re‐admission both in our entire testing cohort and various subpopulations. The area under the receiver operating curve (AUROC) for diagnosis of sepsis was 0.83. Predicted risk scores for patients with septic shock were higher compared with patients with sepsis but without shock (p