Background and Aims Early biliary infection (EBI), as a common complication after endoscopic retrograde cholangiopancreatography (ERCP) combined with biliary stenting in patients with malignant biliary obstruction (MBO), has a significant impact on the survival time and quality of life of patients, and the current research mainly focuses on the risk factors for biliary tract infections, whereas studies on the risk prediction model for the occurrence of EBI are rare. Therefore, based on preoperative clinical data, this study was conducted to construct a risk prediction model of EBI after ERCP combined with biliary stent placement in MBO patients to reduce the incidence of EBI in patients through early and precise preoperative intervention.Methods The clinical data of 285 patients who underwent ERCP combined with biliary stent placement in the Department of Hepatobiliary Surgery of the General Hospital of Ningxia Medical University from January 2018 to September 2021 were retrospectively analyzed (all patients included were diagnosed with MBO by imaging data or pathological evidence). The study endpoint was biliary infection within 30 d after ERCP. The patients were randomized into modeling and validation groups in a 7∶3 ratio. The data of the modeling group were analyzed by univariate analysis and multivariate Logistic regression analysis to construct a predictive model, and artificial neural network (ANN) was used to evaluate the importance of predictor variables. The model was internally and externally validated, and receiver operating characteristic (ROC) and calibration curves were generated to evaluate and test the model's performance.Results The clinical data of 285 patients were included in this study, with 200 cases assigned to the modeling group and 85 cases to the validation group after randomization. Results from univariate and multivariate analyses indicated that location of obstruction (OR=5.942, 95% CI=2.507-14.081, P<0.001), gallstones (OR=4.821, 95% CI=2.087-11.138, P<0.001), diabetes mellitus (OR=5.407, 95% CI=2.067-14.148, P=0.001), and infarct length (OR=1.058, 95% CI=1.028-1.089, P<0.001) were independent risk factors for EBI in MBO patients after ERCP. Logistic regression models were constructed from the independent risk factors and the models were visualized in the form of a nomogram. The assessment of predictive variable weights using ANN ranked them as follows: obstruction length (46.8%), obstruction location (18.6%), diabetes (18.1%), and gallstones (16.5%). The Logistic model underwent internal and external validation, yielding area under the curve (AUC) values of 0.807 and 0.831 and C-index values of 0.807 and 0.831, respectively. The Hosmer-Lemeshow goodness-of-fit test indicated no significant deviations between predicted and actual values (modeling group: P=0.845, validation group: P=0.197).Conclusion According to the ANN evaluation, the constructed Logistic model effectively predicts the risk of post-ERCP EBI occurrence, with obstruction length being identified as the most crucial predictive variable. This model holds potential value for clinical efforts to prevent EBI occurrences. For high-risk patients who might experience postoperative EBI, relevant preemptive measures should be taken before surgery to mitigate the impact of associated risk factors and minimize the incidence of EBI.