Background Timely identification and treatment can greatly enhance the prognosis of patients with distant metastases(DM). This study aims to reveal the risk factors for DM in appendiceal malignant neoplasms patients, and establish a machine learning (ML) model for predicting the risk of DM. Methods A total of 9,376 patients with appendiceal malignant neoplasms, categorized based on the AJCC 8th TNM staging system, were chosen for the study from the Surveillance, Epidemiology, and End Results (SEER) database. Building on this, we developed four machine learning algorithm models along with a Nomogram model. The model was assessed based on confusion matrix, receiver operating characteristic (ROC) curve AUC, calibration curve analysis, and decision curve analysis (DCA). Additionally, the relationship between clinical pathological features and target variables was explored using the SHAP algorithm based on the optimal model. To assess its performance and generalizability, we validated the best model using 52 cases of appendiceal malignant neoplasms from the First Affiliated Hospital of Shantou University Medical College, China. Results Univariate logistic regression analysis and multivariable logistic regression analysis suggested that gender, histological type, grade, T stage, N stage, CEA level, tumor size and distant lymph nodal metastasis were risk factor for DM, while age and race may be related to DM rather than independent risk factors. Five models were constructed incorporated the clinical features. The XGBoost model demonstrated the best performance, achieving an AUC of 0.9917 in the training group and 0.9738 in the internal validation group, respectively. The accuracy was higher than 0.9 in both cohorts. Furthermore, the XGBoost model was evaluated in the outer-validation group, achieving an accuracy of 0.8654 and an AUC of 0.8792. Both the DCA and calibration curves further supported its robust predictive capability. Conclusions Our model was capable of accurately predicting the risk of DM in patients with appendiceal malignant neoplasms, which is crucial for the early identification of high-risk patients and subsequent clinical decision-making.