Abstract Anastomotic leak is a life-threatening complication following colorectal surgery. This study benchmarks Quantum Neural Networks (QNNs) against hyperparameter-tuned classical models (logistic regression, multi-layer perceptrons, boosting algorithms) for anastomotic leak prediction. Using a 200-patient clinical dataset strictly bounded by a priori medical constraints, we simulated QNNs with ZZFeatureMap encoding and EfficientSU2/RealAmplitudes ansatze under realistic hardware noise. To ensure statistical reliability, performance metrics were averaged across 10 independent optimization runs. The EfficientSU2-BFGS configuration achieved the highest mean AUC of \(0.797 \pm 0.024\), while RealAmplitudes with CMA-ES maximized Average Precision (\(0.504 \pm 0.121\)). Crucially, at a fixed, clinically necessary sensitivity of \(83\%\), specific QNN configurations achieved significantly higher specificity (up to \(66\%\)) and Negative Predictive Value (up to \(96\%\)) compared to classical models (maximum \(44\%\) and \(94\%\), respectively), effectively minimizing false positives. However, classical models maintained superior probability calibration for continuous risk stratification. We conclude that QNNs offer robust discriminative performance for clinical screening, warranting further validation on larger, independent cohorts. Similar content being viewed by others Introduction Anastomotic leak is a serious and potentially life-threatening complication arising from surgical procedures involving anastomosis, such as bowel resection, where two ends of a bowel are surgically connected. Anastomotic leak occurs when the connection fails to heal, resulting in leakage of contents into the abdominal cavity. In the case of bowel surgery, this can lead to peritonitis, sepsis, and other severe outcomes. Numerous risk factors, including smoking, malnutrition, immunosuppression, and prolonged operation times, have been associated with an increased likelihood of anastomotic leak. Despite advancements in surgical techniques and perioperative care, accurately predicting and managing the risk of anastomotic leak remains a critical challenge. In this context, leveraging statistical and machine learning methodologies to identify key risk factors and develop predictive models is crucial for improving patient outcomes. ML offers the potential to process