Objective：To identify risk factors associated with in-hospital death and to develop a nomogram-based predictive model for in-hospital mortality in acute myocardial infarction（AMI）patients treated with venous-arterial extracorporeal membrane oxygenation （VA-ECMO）. Methods：A total of 162 consecutive patients with AMI who received VA-ECMO support between May 2021 and June 2025 were retrospectively enrolled. The time of ECMO initiation was defined as the start of follow-up，and patients were followed until hospital discharge or death，whichever occurred first. In - hospital all - cause mortality was defined as the primary endpoint. Cox proportional hazards regression analysis was performed to evaluate the associations between candidate variables and the risk of in - hospital mortality. Variables were selected using least absolute shrinkage and selection operator（LASSO）regression，and a multivariable Cox regression model was subsequently constructed. Based on the final model，a nomogram was developed to predict in- hospital survival probability. Model discrimination was assessed using the concordance index（C -index）. The 28-day time point was used as a fixed landmark for time-dependent receiver operating characteristic（ROC）curve analysis to evaluate short-term predictive performance. Model calibration was evaluated using calibration curves，and clinical utility was assessed using decision curve analysis （DCA）. Results：Multivariable analysis demonstrated that cardiac troponin T，soluble suppression of tumorigenicity - 2（sST2）， hemoglobin concentration，prothrombin time，serum sodium level，and alanine aminotransferase were significantly associated with inhospital mortality. White blood cell count and albumin showed borderline statistical significance in the model. The nomogram incorporating these eight variables exhibited good discriminative performance and satisfactory calibration，indicating favorable clinical applicability. Conclusion：This study identified key clinical variables associated with in-hospital mortality and successfully developed and validated a nomogram - based prediction model. The proposed model provides a simple and reliable tool for individualized risk stratification and may assist clinicians in optimizing decision-making and management strategies for this high-risk population.