Objective：To construct and validate prediction models for all - cause mortality in maintenance hemodialysis（MHD）patients using diverse machine learning algorithms. Methods：Clinical data were collected from 694 patients across four hemodialysis centers in Jiangsu Province，including 591 MHD patients from three tertiary Grade A hospitals in Wuxi City（January 2017-December2023）and 103 patients from one secondary Grade A hospital in Yancheng City（January-December 2024）. The 591 cases were randomly divided into a training set（n=414）and a validation set（n=177）at a 7∶3 ratio for model development and internal validation，while the remaining 103 cases served as a test set for external validation. Predictors were selected via the least absolute selection and shrinkage operator（LASSO）method. Patients were randomly divided into training（n=414）and validation（n=177）sets. Ten machine learning algorithms were employed to develop risk prediction models. Receiver operating characteristic（ROC）curves were plotted to evaluate predictive performance. The calibration accuracy of model-predicted probabilities was assessed using calibration curves，while decision curve analysis（DCA）was employed to quantify the clinical net benefit across varying decision thresholds. External validation utilized the area under the curve（AUC）to assess the generalization capability of the optimal model. 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 AUC was 0.928，the sensitivity was 89.47%，and the accuracy was 0.919，and the evaluation of calibration curve and DCA showed that the consistency and benefit of the model are still good，the Brier score of 0.089 indicates that the model demonstrates low predictive error and favorable calibration performance on the internal validation dataset，suggesting its reliability in probabilistic forecasting. External validation yielded an AUC of 0.835，indicating robust generalization capability of the model. 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<300 pg/mL，age，junior high school education or lower，blood urea nitrogen -to - creatinine ratio，diabetic nephropathy，college degree or higher education and sex. Conclusion：The SVM-based prediction model demonstrates robust performance in forecastingall-cause mortality among MHD patients，facilitating early identification of high-risk individuals and supporting clinical decision-making.