Objective: To develop a predictive model for the incidence of Endoscopic Retrograde Cholangiopancreatography (ERCP) following emergency laparoscopic cholecystectomy, utilising advanced machine learning techniques. Also, the associated factors between these procedures are to be identified. Background: Laparoscopic cholecystectomy is the preferred treatment for symptomatic cholelithiasis and acute cholecystitis, with increasing applications even in severe cases. The necessity for postoperative ERCP to manage choledocholithiasis or biliary injuries poses significant clinical challenges. This study aims to develop a predictive model for the incidence of ERCP following emergency laparoscopic cholecystectomy using advanced machine learning techniques. Method: We conducted a retrospective cohort study utilising the Tokushukai Medical Database, which includes data from 42 hospitals over a decade in Japan. The study population consisted of adult patients undergoing emergency laparoscopic cholecystectomy. We employed four machine learning models—logistic regression, random forest, gradient-boosting decision trees (GBDT), and multilayer perceptrons - on a dataset divided into training/validation and testing groups. We also calculated Shapley additive explanation values for the GBDT to identify the significant variables. Result: Out of 9,695 patients, 8,854 met the inclusion criteria. The incidence of postoperative ERCP was 5.7% and 6.4% in the training/validation and testing datasets, respectively. The GBDT demonstrated superior performance, with the highest predictive capacity for postoperative ERCP. Significant predictors identified included common bile duct dilatation, serum albumin, and lactate dehydrogenase levels. Conclusion: This study successfully established a robust predictive model for ERCP following emergency laparoscopic cholecystectomy and identified associated factors with the outcome.