Background <p>Early identification of bowel necrosis in patients with incarcerated groin hernia (IGH) remains clinically challenging, yet is crucial for timely surgical decision-making and improved outcomes. Reliable early risk stratification tools are currently lacking.</p> Methods <p>We conducted a retrospective cohort study of patients surgically treated for IGH between January 2014 and December 2025. Using routinely available admission data, a rigorous three-stage feature selection strategy was applied within the training cohort to identify core predictors. Seven machine learning models were developed and internally validated using ten-fold cross-validation with random over-sampling. Model performance was evaluated using discrimination, calibration, and decision curve analyses. Model interpretability was assessed using SHapley Additive exPlanations (SHAP), and a nomogram was constructed based on logistic regression.</p> Results <p>A total of 220 patients were included, of whom 79 (35.9%) developed bowel necrosis requiring resection. Five core predictors were consistently identified: bowel obstruction, time from onset to admission, VAS score, white blood cell count, and serum sodium level. Among all models, the gradient boosting machine achieved the highest discriminative performance in the test cohort (AUROC = 0.919; AUPRC = 0.818), while the logistic regression model demonstrated excellent calibration and clinical interpretability. SHAP analysis confirmed the relative importance and directional effects of the selected predictors.</p> Conclusions <p>This study presents a parsimonious and interpretable machine learning framework for early prediction of bowel necrosis in IGH. The proposed models may support timely surgical decision-making and improve risk stratification in emergency surgical practice.</p>