Background　Early　prediction　of　heart　failure　(HF)　after　acute　myocardial　infarction　(AMI)　is　essential　for　personalized　treatment.　We　aimed　to　use　interpretable　machine　learning　(ML)　methods　to　develop　a　risk　prediction　model　for　HF　in　AMI　patients.Methods　We　retrospectively　included　patients　initially　with　AMI　who　received　percutaneous　coronary　intervention　(PCI)　in　our　hospital　from　November　2016　to　February　2020.　The　primary　endpoint　was　the　occurrence　of　HF　within　3　years　after　operation.　For　developing　a　predictive　model　for　HF　risk　in　AMI　patients,　the　least　absolute　shrinkage　and　selection　operator　(LASSO)　Regression　was　used　to　feature　selection,　and　four　ML　algorithms　including　Random　Forest　(RF),　Extreme　Gradient　Boost　(XGBoost),　Support　Vector　Machine　(SVM),　and　Logistic　Regression　(LR)　were　employed　to　develop　the　model　on　the　training　set.　The　performance　evaluation　of　the　prediction　model　was　carried　out　on　the　training　set　and　the　testing　set,　utilizing　metrics　including　AUC　(Area　under　the　receiver　operating　characteristic　curve),　calibration　plot,　and　decision　curve　analysis　(DCA).　In　addition,　we　used　the　Shapley　Additive　Explanations　(SHAP)　value　to　determine　the　importance　of　the　selected　features　and　interpret　the　optimal　model.Results　A　total　of　1220　AMI　patients　were　included　and　244　(20%)　patients　developed　HF　during　follow-up.　Among　the　four　evaluated　ML　models,　the　XGBoost　model　exhibited　exceptional　accuracy,　with　an　AUC　value　of　0.922.　The　SHAP　method　showed　that　left　ventricular　ejection　fraction　(LVEF),　left　ventricular　end-systolic　diameter　(LVDs)　and　lactate　dehydrogenase　(LDH)　were　identified　as　the　three　most　important　characteristics　to　predict　HF　risk　in　AMI　patients.　Individual　risk　assessment　was　performed　using　SHAP　plots　and　waterfall　plot　analysis.Conclusions　Our　research　demonstrates　the　potential　of　ML　methods　in　the　early　prediction　of　HF　risk　in　AMI　patients.　Furthermore,　it　enhances　the　interpretability　of　the　XGBoost　model　through　SHAP　analysis　to　guide　clinical　decision-making.