Background: Surgical site infections (SSIs) aresignificant complications following laparoscopic surgery, leading to increased morbidity and healthcare costs. Despite advancements, SSIs remain prevalent, necessitating accurate prediction models to improve patient safety and surgical care efficiency. Methods: A prospective cohort study was conducted, including patients undergoing elective laparoscopic surgery. Data on demographics, comorbidities, surgical details, and outcomes were collected. Univariate and multivariate analyses identified predictors of SSIs. Model performance was assessed using the area under the receiver operating characteristic curve (AUC-ROC) and calibration plots, internally validated using bootstrapping.