BACKGROUND: Cardiac valve calcification (CVC) is common in dialysis patients and associated with increased cardiovascular risk. However, early screening has been limited by cost concerns. This study aimed to develop and validate a machine learning model to enhance early detection of CVC. METHODS: Data were collected at four centers between 2020 and 2023, including 852 dialysis patients in the development dataset and 661 in the external validation dataset. Predictive factors were selected using LASSO regression combined with univariate and multivariate analyses. Machine learning models including CatBoost, XGBoost, decision tree, support vector machine, random forest, and logistic regression were used to develop the CVC risk model. Model performance was evaluated in both validation sets. Risk thresholds were defined using the Youden index and validated in the external dataset. RESULTS: In the development dataset, 32.9% of patients were diagnosed with CVC. Age, dialysis duration, alkaline phosphatase, apolipoprotein A1, and intact parathyroid hormone were selected to construct the CVC risk prediction model. CatBoost exhibited the best performance in the training dataset. The logistic regression model demonstrated the best predictive performance in both internal and external validation sets, with AUROCs of 0.806 (95% CI 0.750-0.863) and 0.757 (95% CI 0.720-0.793), respectively. Calibration curves and decision curves confirmed its predictive accuracy and clinical applicability. The logistic regression model was selected as the optimal model and achieved excellent risk stratification in CVC risk prediction. CONCLUSION: The predictive model effectively identifies CVC risk in dialysis patients and offers a robust tool for early detection and improved management.