Add Publication. Infective endocarditis, a life-threatening condition, poses significant challenges for early diagnosis and personalized treatment due to insufficient biomarkers and limited understanding of its pathophysiology. Here, we analyzed plasma and vegetation proteomes from 238 patients with infective endocarditis and 100 controls, with two external validation cohorts. We developed machine learning-based diagnostic and prognostic models for infective endocarditis, with area under the curve values of 0.98 and 0.87, respectively. Leucine-rich alpha-2-glycoprotein 1 and NADH:ubiquinone oxidoreductase subunit B4 are potential biomarkers associated with infection severity. Pathologically, protein networks characterized by glycometabolism, amino acid metabolism, and adhesion are linked to adverse events. Liver dysfunction may exacerbate the condition in patients with severe heart failure. Neutrophil extracellular traps emerge as promising therapeutic targets in Streptococcus or Staphylococcus aureus infections. Collectively, our findings provide significant insights into biomarker discovery and underlying pathophysiological mechanisms in infective endocarditis, advancing early diagnosis and personalized medicine.