Background <p>The global prevalence of gestational diabetes mellitus (GDM) continues to rise, and GDM significantly increases the risk of spontaneous preterm birth (sPTB), posing serious threats to maternal and neonatal health. Existing models for assessing sPTB risk in patients with GDM remain limited in accuracy, nonlinear relationship capture, and the handling of complex feature interactions. This study aimed to systematically evaluate the performance differences between eight individual machine learning algorithms and a Stacking ensemble learning architecture, and to achieve risk identification and stratification through optimized model-building strategies.</p> Methods <p>Clinical data were collected from 661 patients with GDM who were hospitalized for delivery at the Affiliated Hospital of Xuzhou Medical University from March 2021 to March 2025. The patients were randomly divided into a training set (<i>n</i> = 463) and a testing set (<i>n</i> = 198) at a 7:3 ratio. Clinical characteristics were initially screened using univariate analysis. LASSO regression and the Boruta algorithm were then jointly used to determine the full-set feature indicators, and Wald tests were further applied to identify independent risk factors and construct the reduced-set feature space. Based on the two feature sets, eight individual machine learning models were developed, including logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), naïve bayes (NB), artificial neural network (ANN), and gradient boosting machine (GBM). To address the limitations of individual models in calibration and generalization performance, a Stacking ensemble learning framework was introduced for model fusion optimization, and a visual online risk calculator was developed.</p> Results <p>LASSO regression and the Boruta algorithm selected nine variables for the full-set feature group: fasting plasma glucose on OGTT, 1-hour post-load glucose, white blood cell count (WBC), neutrophil-to-lymphocyte ratio (NLR), hemoglobin (Hb), fibrinogen (FIB), alanine aminotransferase (ALT), alkaline phosphatase (ALP), and uric acid-to-creatinine ratio (UA/Cr). Wald tests confirmed that ALP, ALT, fasting plasma glucose (FPG), UA/Cr, and Hb were independent influencing factors and constituted the reduced-set core indicators. Comparison of individual models showed that the full-set GBM model performed well in risk recognition, whereas the LR model had advantages in calibration and clinical applicability. The Stacking ensemble model integrating GBM and LR showed the best overall performance, with an AUC of 0.879 and accuracy, sensitivity, and specificity of 0.768, 0.767, and 0.768, respectively, in the testing set. Two patients were selected to apply the online sPTB risk calculator for patients with GDM based on the ensemble model. The estimated risk was 3.26% for a non-sPTB patient and 93.96% for a patient with sPTB.</p> Conclusion <p>The Stacking ensemble learning model showed good assessment performance in the present dataset and may serve as an exploratory model for near-delivery sPTB risk stratification in patients with GDM. The online risk calculator developed from this model may support preliminary near-delivery risk stratification in patients with GDM; external validation is required before clinical implementation.</p>