Objective <p>This systematic review provides an overview of machine learning (ML) methods for predicting diabetic peripheral neuropathy (DPN).</p> Method <p>We searched PubMed, Embase, Cochrane Library, and Web of Science databases with the search period limited from their inception to December 3, 2024 (the last search date). The search terms were restricted to “diabetes,” “neuropathy,” and “machine learning.” All studies that developed or validated prognostic models for DPN using ML were considered. Prediction model Risk of Bias ASsessment Tool (PROBAST) was used to assess the risk of bias and applicability of included studies.</p> Results <p>A total of 888 studies were retrieved and 15 articles were included. Most were retrospective studies, with sample sizes ranging from 90 to 102,876 patients. All 15 studies utilised internal validation methods, three studies employed both internal and external validation methods. Internal validation methods like cross-validation were widely used, with area under the curve (AUC) ranging from 0.640 to 0.900. A total of six studies reported complete AUC values yielding a pooled AUC of 0.773 (CI: 0.707–0.839, I²= 99.14). A total of 34 different ML algorithms were utilised across the studies, with the top five being logistic regression, random forest, support vector machine, decision tree, and XGBoost. Calibration was reported in 6 studies, showing satisfactory performance. All studies had a high risk of bias, but most models demonstrated good applicability.</p> Conclusion <p>Existing DPN prediction models demonstrate good performance in discrimination. However, the evaluation indicates that the overall risk of bias in the included studies is high, and their applicability is limited. Future efforts should prioritize prospective, large, multicentre datasets, external validation, and adherence to PROBAST guidelines to reduce bias and enhance applicability for clinical application.</p>