Background <p>Patients with gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs) and liver metastases typically exhibit poor prognoses. However, accurate survival prediction models remain insufficient. This study aimed to develop machine learning-based models to predict the 1-year, 3-year, and 5-year overall survival in these patients.</p> Methods <p>We retrospectively analyzed patients diagnosed with GEP-NENs and liver metastases from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into training and testing sets in a 7:3 ratio. Seven machine learning models were constructed: cox regression, lasso regression, random survival forest (RSF), extreme gradient boosting (XGBoost), decision tree, gradient boosting machine (GBM), and neural network. Model performance was evaluated using C-index, AUC, Calibration curve, Brier score, and decision curve analysis (DCA). The optimal model was further interpreted through variable importance analysis, partial dependence plots, and individual prediction plots.</p> Results <p>A total of 4,528 patients were included, with 3,165 in the training set and 1,363 in the testing set. Among the seven models, the RSF model demonstrated the best overall performance. In the training set, it achieved a C-index of 0.815, with 1-year, 3-year, and 5-year AUC values of 0.895, 0.907, and 0.905, respectively, and Brier scores of 0.121, 0.128, and 0.128. The calibration curve shows good predictive performance, while the DCA highlights its strong net benefit in clinical decision-making. In the testing set, it maintained robust performance (C-index: 0.785; AUC: 0.855/0.859/0.841). The five most influential variables in the RSF model were tumor grade, surgical intervention, tumor site, age, and histology.</p> Conclusion <p>The RSF model provides a reliable tool for predicting overall survival in GEP-NEN patients with liver metastases.</p>