Objective <p>Patients undergoing coronary artery bypass grafting (CABG) with cardiopulmonary bypass (CPB) are at high risk of developing postoperative pulmonary complications (PPCs). This study aimed to develop and validate a clinical prediction model for these complications after CABG.</p> Methods <p>In total, 849 patients were randomly divided into training (<i>n</i>=594) and validation (<i>n</i>=255) sets in a 7:3 ratio. We used least absolute shrinkage and selection operator (LASSO) regression to identify predictive variables, incorporated them into a multivariable logistic regression model, and developed a nomogram. Model performance was assessed through discrimination (receiver operating characteristic (ROC) curve analysis, area under the curve (AUC)), calibration (calibration curves, maximum calibration error (Emax), average calibration error (Eavg)), and clinical utility assessment (decision curve analysis).</p> Results <p>Five predictive indicators were selected: age, smoking history, diabetes mellitus, emergent surgery, and anesthesia duration. The model demonstrated excellent predictive performance, with an AUC of 0.902 (0.859–0.945) for the training set and 0.864 (0.811–0.917) for the validation set. Calibration curve results showed non-significant <i>P</i>-values from the unreliability test (<i>P</i> = 0.861 for training set, <i>P</i> = 0.741 for validation set), indicating excellent calibration. Emax and Eavg values were 0.042 and 0.013 for the training set, and 0.046 and 0.009 for the validation set, respectively, showing a strong agreement between the predicted values and actual observations.</p> Conclusion <p>An original nomogram accurately predicted PPCs after CABG with CPB, which enables clinicians to rapidly assess PPC risk for individual patients without complex calculations, providing objective, quantitative evidence for preoperative risk evaluation, informed consent discussions, and perioperative management.</p> Graphical Abstract <p></p>