Abstract Background: The incidence of small intestine neuroendocrine neoplasms (SI-NEN) has increased significantly, posing challenges in early diagnosis and effective management due to non-specific symptoms and complex tumor biology, especially in predicting distant metastasis (DM). Methods: This retrospective study analyzed 3,157 patients diagnosed with SI-NEN from 2005 to 2015 using the Surveillance, Epidemiology, and End Results (SEER) database. We employed multifactorial logistic regression to identify independent risk factors for DM and developed several machine learning models to predict its occurrence. These included a suite of nine key models: XGBoost, Logistic Regression, LightGBM, Random Forest, Complement Naive Bayes, Multi-Layer Perceptron Classifier, Decision Tree, Gradient Boosting Decision Tree, and Support Vector Machine, all validated through k-fold cross-validation and hyperparameter optimization. Furthermore, we extended our analysis to survival studies to identify prognostic factors that may significantly influence patient outcomes. Results: Logistic regression demonstrated the highest efficacy, achieving an AUC of 0.774 in the training set and 0.747 in the validation set, values which are considered high, indicating superior performance in detecting early DM. Additionally, a machine learning-enhanced clinical nomogram was constructed, incorporating individual patient characteristics for personalized treatment planning. Survival analysis identified key prognostic indicators, and the resulting prognostic nomogram displayed high predictive accuracy, validated through calibration curves and decision curve analysis. Conclusion: The study underscores the utility of advanced predictive models in enhancing the diagnostic and prognostic assessment of SI-NEN, suggesting a framework for future clinical application and continuous improvement of these models. The developed predictive and prognostic nomograms provide crucial tools for clinical decision-making, potentially improving overall survival and quality of life by facilitating personalized treatment strategies based on detailed risk profiles. Keywords: SI-NEN, distant metastasis, machine learning, logistic regression, prognostic nomogram, survival analysis, SEER database.