Epidemiological trends indicate a concerning rise in early-onset cases of myocardial infarction (MI). We aimed to assess and compare the ability of plasma proteomics, conventional risk factors, and polygenic risk score (PRS) for the risk prediction of early-onset myocardial infarction (EOMI). Included were 13,097 participants aged 50 or younger, without prevalent cardiovascular diseases. The participants were randomly divided into training and validation sets. EOMI was defined as MI diagnosed before age 55. In the training set, 2,093 plasma proteins were assessed for the associations with incident EOMI using Cox proportional hazards regression models. Important proteins were selected by the least absolute shrinkage and selection operator (LASSO) regression to develop protein-based models. The predictive performance of protein-based models, conventional risk factors, and PRS, either alone or as combinations, was assessed in the validation set. Two protein-based models were constructed using 22 key proteins selected by LASSO. Each standard-deviation increment of a weighted protein score was associated with a 2.57-fold higher risk of EOMI. Incorporating this protein score (ΔC-index = 0.125; 95% CI: 0.040, 0.213), a protein panel (ΔC-index = 0.189; 95% CI: 0.065, 0.276), or other conventional risk factors (ΔC-index = 0.158; 95% CI: 0.039, 0.239) each significantly improved the predictive performance over a basic model including age, sex, and race/ethnicity, whereas adding PRS did not. The combination of the protein panel and conventional risk factors demonstrated the best discrimination ability (C-index = 0.875; 95% CI: 0.814, 0.935). Plasma proteomics enhanced the risk prediction for EOMI beyond conventional risk factors and PRS. These findings may have implications for risk stratification and personalized prevention which prevent or delay the onset of myocardial infarction among relatively younger population.