Objective  To develop a prediction model for sleep disorders in maintenance peritoneal dialysis (PD) patients based on machine learning (ML) algorithms.  Methods  A multicenter cross-sectional study was conducted, enrolling 578 PD patients admitted to 20 hospitals, including Xinjiang Uygur Autonomous Region People's Hospital and The Second People's Hospital of Kashi Prefecture, from January to December 2024. Based on the pittsburgh sleep quality index (PSQI), the patients were divided into a non-sleep disorder group (total score ＜ 7) and a sleep disorder group (score≥ 7), and randomly assigned to a training set and an internal validation set at a 6:4 ratio. Univariate logistic regression (ULR), the random forest-based Boruta algorithm, and the least absolute shrinkage and selection operator (LASSO) were used to identify key predictive indicators. The union of these variables was further screened through multivariate logistic regression (MLR). The ultimately retained feature variables were used to train six machine learning models to identify the optimal model, and its interpretability was evaluated using Shapley additive explanations (SHAP). Results  MLR confirmed that age (OR=1.029, 95% CI: 1.011～1.049, P=0.002), pruritus (OR=2.266, 95%CI: 1.325～3.970, P=0.003), anxiety (OR=4.075, 95% CI: 2.399～7.012, P＜0.001), Kt/V (OR=2.065, 95%CI: 1.210～3.577, P=0.008), and serum calcium (OR=7.476, 95% CI: 2.614～22.610, P＜0.001) were independent risk factors for sleep disorders in PD patients. Among the constructed models, the extreme gradient boosting (XGB) model performed best, achieving an area under the receiver operating characteristic (ROC) curve of 0.830 (95% CI: 0.780～0.870) in the training set and 0.660 (95%CI: 0.560～0.760) in the internal validation set, with an accuracy of 0.800. SHAP plots indicated that total protein, serum calcium, and age were the three most important predictive features.   Conclusions  This study successfully developed the first XGB-based prediction model for sleep disorders among PD patients in China, facilitating the early identification of high-risk populations and supporting precise interventions.