AIM: To construct a network calculator based on machine learning (ML) models to predict the risk of delayed awakening from anaesthesia in breast cancer (BC) patients. METHODS: A total of 435 BC patients surgically treated at our hospital from January 2023 to June 2024 were selected. The Boruta algorithm was used to screen for important characteristic variables for the risk of delayed awakening from anaesthesia.All patients were randomly assigned to a training set (n=261) and a test set (n=174) based on a 3:2 ratio and nine ML models were constructed and trained. Nine ML models were evaluated on the basis of receiver operating characteristic (ROC) curves for a random sample of 10 subjects and the clinical utility of the models was assessed using decision curve analysis.Combined with SHapley Additive exPlanations (SHAP) bar graphs, summary graphs and force diagrams additional interpretation and visualization of the ML model.Construction of a network calculator for predicting the risk of delayed awakening from anesthesia in BC patients using the R package. RESULTS: Of the 435 BC patients, 25.1% experienced delayed awakening from anesthesia.Boruta algorithm screened seven feature variables.The ROC curve shows that the XGBoost model has the highest area under the curve (AUC) for 10 random samples among the 9 ML models, and the decision curve shows that the XGBoost model has a significant clinical net benefit.The SHAP bar graph shows the importance of ASA classification, surgery time, anesthesia time, intraoperative blood loss, propofol, preoperative anemia, and intraoperative hypothermia, and the SHAP summary graph reflects the distribution of the ranges of influence of the seven important characteristic variables, which are "separated at the ends."The SHAP force diagram visualization XGBoost model predicted the risk of delayed awakening from anesthesia for individual patients with a predictive value of 0.998 for patients with delayed awakening from anesthesia and 0.008 91 for patients without delayed awakening from anesthesia.A web-based calculator (https://xz-nomogram.shinyapps.io/DE_web/) based on an interpretable XGBoost model effectively predicts the risk of delayed awakening from anesthesia in BC patients. CONCLUSION: ASA classification, surgery time, propofol, intraoperative blood loss, anaesthesia time, preoperative anaemia and intraoperative hypothermia are important characteristic variables for the risk of delayed awakening from anaesthesia in BC patients. The network calculator based on the interpretable XGBoost model can accurately and quickly quantify the risk of delayed awakening from anaesthesia, which can help clinicians to effectively adjust the treatment strategy and better improve the prognosis of patients.