Objective To construct and validate a risk prediction model for skin tears in cardiac surgery patients, and provide a reference for developing preventive intervention programs. Methods A prospective research design was adopted, with 750 postope-rative cardiac surgery patients selected from June 2022 to December 2023 as study subjects. Seventy percent of the patients were included in the modeling group (525 cases), and thirty percent were included in the validation group. Univariate and multivariate logistic regression analyses were conducted to identify the risk factors for skin tears in postoperative cardiac patients, and a risk prediction model along with a nomogram was established. The predictive performance of the model was subsequently validated. Results In the modeling group, 122 cases experienced skin tears, resulting in an incidence rate of 23.2%. Regression analysis indicated that age, history of diabetes, history of skin tears, ecchymosis, hypoxemia, incontinence-associated dermatitis, delirium, and the number of corticosteroid uses were independent risk factors for skin tears in postoperative cardiac patients (all P<0.05). A nomogram was created using these eight factors as independent variables, with the area under the ROC curve for the model being 0.807, sensitivity 0.691, and specificity 0.802. In the validation group, the ROC curve area was 0.801, with sensitivity at 0.776 and specificity at 0.705. Conclusion The constructed model demonstrates good predictive performance, providing clinical healthcare professionals with a reference for early and rapid identification of the risk of skin tears in postoperative cardiac patients and timely implementation of preventive intervention programs.