Objective：To develop and externally validate a patient - level prediction model for postoperative frailty in adults undergoing cardiac surgery. Methods：Our study included patients who underwent cardiac surgery at the First Affiliated Hospital of Nanjing Medical University between January and December 2023. Frailty was assessed one month postoperatively using the FRAIL scale，and patients were categorized into frailty and non-frailty groups. Three machine learning algorithms，least absolute shrinkage and selection operator（LASSO），random forest（RF），and extreme gradient boosting（XGBoost），were employed to identify common predictors. A nomogram prediction model was subsequently constructed using Logistic regression. The model’s discriminative ability was evaluated using the receiver operating characteristic（ROC）curve and the area under the curve（AUC）. Calibration curves assessed consistency，and decision curve analysis（DCA）evaluated clinical utility. The model was validated internally and externally.Results：A total of 301 patients were included. Among them，235 patients were divided into a training set（n=165）and an internal validation set（n=70）at a ratio of 7∶3，while the remaining 66 patients served as temporal validation. Based on the machine learning results，four common predictors were identified：age，preoperative left ventricular ejection fraction（LVEF），preoperative albumin level，and preoperative left ventricular end - diastolic dimension（LVDd）. These were used to construct the nomogram. The modeldemonstrated excellent discriminative ability in the training set（AUC=0.846，95% CI：0.763-0.928），internal validation set（AUC=0.821，95%CI：0.701-0.940），and temporal validation set（AUC=0.846，95%CI：0.740-0.951）. The calibration curve indicated high consistency between predicted and observed risks. Decision curve analysis further confirmed its good clinical practicality. Conclusion：The nomogram prediction model for post-cardiac surgery frailty，based on patient age，preoperative albumin level，LVEF，and LVDd，exhibits good predictive performance and clinical applicability，facilitating the early identification of high-risk patients.