PURPOSE: This study develops and validates an early clinical prediction model for gestational diabetes mellitus (GDM) specifically in women of increased maternal age with singleton pregnancies, thereby facilitating early identification of high-risk individuals and timely targeted intervention. METHODS: This retrospective cohort study included 338 women of increased maternal age with singleton pregnancies (82 diagnosed with GDM) who received prenatal care at the First People's Hospital of Changde between January 2023 and June 2024, comprising the training set. An additional 232 women (70 with GDM) seen between July 2024 and June 2025 comprised the validation set. Clinical data were collected and risk factors for GDM were identified using logistic regression analysis. A prediction model was constructed using R software. Model performance was assessed using receiver operating characteristic (ROC) curves, with internal validation performed via K-fold cross-validation, and external validation carried out in the independent validation set. RESULTS: A significant between-group difference was observed in assisted reproductive technology history between the training and validation sets (Z = 8.631, P = 0.003). In the training set, pre-pregnancy body mass index (BMI, calculated as weight in kilograms divided by the square of height in meters), fasting plasma glucose, uric acid, and triglyceride levels differed significantly between the GDM and non-GDM groups (P < 0.05 for all). Multivariate logistic regression identified all four parameters as independent risk factors for GDM (P < 0.05). The ROC curve analysis yielded an area under the curve of 0.773 for the training set and 0.808 for the validation set, indicating good predictive performance. CONCLUSION: We established a GDM prediction model for women of increased maternal age with singleton pregnancies, incorporating four readily available clinical indicators. The model demonstrated satisfactory discrimination and generalizability upon both internal and external validation.