Abstract: Objective：To construct prediction models for all-cause mortality in maintenance hemodialysis (MHD) patients in Wuxi City using diverse machine learning algorithms.Methods：Clinical data from 591 MHD patients across three tertiary hospitals in Wuxi City (January 2017 to December 2023) were collected.Predictors were selected via the least absolute selection and shrinkage operator (LASSO) method. Patients were randomly divided into training (n=414，70%) and validation (n=177，30%) sets. Ten machine learning algorithms were employed to develop risk prediction models. Receiver operating characteristic (ROC) curves were plotted to evaluate predictive performance. Shapley Additive exPlanations (SHAP) were applied to rank variable importance.Results：The all-cause mortality rate was 42.6%(252/591).Among the 10 models，the support vector machine (SVM) exhibited optimal performance，the area under the ROC curve (AUC) was 0.928，the sensitivity was 0.895，and the accuracy was 0.919.The SHAP plot showed that the importance ranking of the influencing factors for all-cause mortality was living alone，tunneled cuffed catheter (TCC)，prealbumin，albumin，Charlson Comorbidity Index (CCI) score，iPTH<300pg/mL，age，middle school education and lower，blood urea nitrogen-to-creatinine ratio，diabetic nephropathy，college degree or higher education and gender.Conclusion：The SVM-based prediction model demonstrates robust performance in forecasting all-cause mortality among MHD patients, facilitating early identification of high-risk individuals and supporting clinical decision-making.