Abstract: Objective: To construct a meta-analysis-based risk prediction model for nosocomial infections in adult patients undergoing extracorporeal membrane oxygenation (ECMO), thereby providing an assessment tool to evaluate and reduce the risk of nosocomial infections in this population. Methods: Databases were systematically searched from inception to June 20, 2025, for relevant literature on risk factors associated with nosocomial infections in adult ECMO patients. The quality of included studies was evaluated using the Newcastle-Ottawa Scale (NOS). Meta-analysis was performed with Review Manager 5.4, and integrated risk values of identified factors were used to construct a logistic regression prediction model. Patients who underwent ECMO treatment in a tertiary care hospital from January 2023 to August 2024 were enrolled as the model validation cohort. The model performance was assessed using receiver operating characteristic (ROC) curves, Hosmer-Lemeshow test, calibration curves, and decision curve analysis (DCA). Results: Twenty-six literatures, involving 3,872 patients were included, and the overall incidence of nosocomial infections in adult ECMO patients was 34.19%. The logistic regression model was constructed as follows: Logit (P)=ɑ-0.02×age+0.09×BMI+0.08×duration of ECMO support+ 0.27×duration of mechanical ventilation+0.02×duration of central venous catheterization+2.06×SOFA score+ 1.07×CRRT use+1.78×IABP use. The sensitivity and specificity of the model were 80.0% and 68.9%, respectively. The area under the ROC curve (AUC) was 0.777 (95% CI: 0.659-0.894), indicating good discrimination. The Hosmer-Lemeshow test showed satisfactory model calibration (χ²=8.325, P=0.402). Calibration curve analysis revealed a prediction error between the predictive model and the actual observations was 0.013, indicating high accuracy and consistency. DCA demonstrated a positive net benefit, suggesting favorable clinical utility. Conclusion: The meta-analysis-based risk prediction model for nosocomial infections in adult ECMO patients demonstrates strong predictive performance. It serves as a useful tool for early identification of patients at high risk for nosocomial infections.