Plasma was collected from 251 participants, including patients with EOC (n = 97), borderline ovarian tumors (n = 38), benign ovarian tumors (n = 54), and healthy controls (n = 62). Proteomic and metabolomic profiling was performed. A machine learning model was trained on a training cohort (34 EOC patients and 62 non-OC individuals [borderline, benign, and healthy controls]) to distinguish EOC from other groups. The model was validated in two independent cohorts: validation cohort 1 (n = 25) and validation cohort 2 (n = 130) using targeted proteomics and untargeted metabolomics.