<p>Postoperative complications following laparoscopic general surgery contribute significantly to patient morbidity, mortality, and healthcare costs. This study develops and evaluates machine learning and deep learning models to predict six critical postoperative complications: cardiac arrest, myocardial infarction, pulmonary embolism, reintubation, pneumonia, and failure to wean from ventilatory support. Using a deidentified dataset of 210,349 patient records, we implemented a comprehensive classification pipeline to address the substantial class imbalance inherent in surgical complications data. The pipeline incorporated preprocessing techniques, synthetic minority oversampling, and systematic evaluation of machine learning algorithms and deep learning architectures. Model performance was assessed using area under the curve (AUC) and recall metrics, with particular emphasis on maximizing the detection of true positive cases given the clinical importance of early intervention. To complement these metrics Receiver Operator Characteristic (ROC) visualizations and confusion matrices were provided. We compared the performance of different models across the six complications and identified which approaches were most effective for specific adverse outcomes. Our findings provide insights into the relative value of model complexity versus interpretability in clinical prediction tasks and highlight important considerations for the implementation of predictive analytics in surgical care. This research contributes to the advancement of predictive analytics in postoperative care and offers practical recommendations for clinical integration to improve surgical outcomes through early intervention and optimized resource allocation.</p>