Up to 30% of patients with acute coronary syndrome (ACS) die from adverse events, mainly renal failure and myocardial infarction (MI). Accurate prediction of adverse events is therefore essential to improve patient prognosis. Machine learning (ML) methods can accurately identify risk factors and predict adverse events. A total of 5240 patients diagnosed with ACS who underwent PCI were enrolled and followed for 1year. Support vector machine, extreme gradient boosting, adaptive boosting, K-nearest neighbors, random forest, decision tree, categorical boosting, and linear discriminant analysis (LDA) were developed with 10-fold cross-validation to predict acute kidney injury (AKI), MIduring hospitalization, and all-cause mortality within 1year. Features with mean Shapley Additive exPlanationsscore >0.1 were screened by XGBoost method as input for model construction. Accuracy, F1 score, area under curve (AUC), and precision/recallcurve were used to evaluate the performance of the models. Overall, 2.6% of patients died within 1year, 4.2% had AKI, and 4.7% had MI during hospitalization. The LDA model was superior to the other sevenML models, with an AUC of 0.83, F1 score of 0.90, accuracy of 0.85, recall of 0.85, specificity of 0.68, and precision of 0.99 in predicting all-cause mortality. For AKI and MI, the LDA model also showed good discriminating capacity with an AUC of 0.74. The LDA model, using easily accessible variables from in-hospital patients, showed the potential to effectively predict the risk of adverse events and mortality within 1year in ACS patients after PCI.