OBJECTIVE: To evaluate the incidence of pleural effusion (PE) following coronary artery bypass grafting (CABG), identify associated risk factors, and develop a validated predictive model for early detection. METHODS: A retrospective cohort of 1,979 patients who underwent CABG at Beijing Chaoyang Hospital (Capital Medical University) was randomly divided into training (70%) and validation (30%) sets. Risk factors for PE were identified through univariate analysis, LASSO regression, and multivariate logistic regression. Five machine learning models-nomogram, back-propagation neural network (BPNN), random forest, gradient boosting, and support vector machine-were developed. External validation was performed using data from 289 patients at the First Affiliated Hospital of Guangxi Medical University. RESULTS: PE occurred in 71.0% of patients (1,405/1,979) within 3 days postoperatively. Independent risk factors included body mass index (BMI), carotid artery stenosis, postoperative pneumonia, duration of mechanical ventilation, intraoperative blood loss, operative time, and ejection fraction. Among the models, the BPNN demonstrated the best performance, with area under the curve (AUC) values of 0.828 in the training set and 0.751 in the internal validation set. The AUC for external validation was 0.737, outperforming the other models across all evaluation metrics. CONCLUSIONS: This study developed a predictive model for post-CABG pleural effusion with high discriminatory power, providing a useful tool for early risk stratification in clinical settings.