Objective To identify risk factors for death in patients with rhabdomyolysis-induced acute kidney injury (RI-AKI) treated with continuous renal replacement therapy (CRRT), then to develop and validate the efficacy of prediction models based on these risk factors. Methods Clinical data and prognostic information of patients with RI-AKI requiring CRRT from 2008 to 2019 were extracted from the MIMIC-IV 2.2 database. The enrolled patients were divided into a training set and a test set at a ratio of 7∶3. LASSO regression, random forest (RF) and extreme gradient boosting (XGBoost) were used to identify the risk factors affecting patients’ 28-day survival in the training set, then to develop logistic model, RF model, support vector machine (SVM) model and XGBoost model. The accuracy of above prediction models and the area under the receiver operating characteristic curve (AUC) were calculated in the test set. Results A total of 175 patients were included. Lactic acid, age, Acute Physiology Score Ⅲ, hemoglobin, mean arterial pressure and body mass index measured at intensive care unit admission were identified as the six risk factors affecting 28-day survival of enrolled patients by LASSO regression, RF and XGBoost. The accuracy of the logistic model, RF model, SVM model and XGBoost model in the test set was 0.75, 0.79, 0.79 and 0.81, with the AUC of 0.82, 0.85, 0.87 and 0.87, respectively. Conclusion The XGBoost model, incorporating six risk factors including lactic acid, age, Acute Physiology Score Ⅲ, hemoglobin, mean arterial pressure, and body mass index assessed at the time of admission to the intensive care unit, demonstrates superior clinical predictive performance, thereby enhancing the clinical decision-making process for healthcare professionals.