The transformative role of artificial intelligence in gestational diabetes mellitus: advancements in screening, management, and long-term outcome prediction.

Purpose: Artificial intelligence (AI) has emerged as a pivotal tool in enhancing the management of gestational diabetes mellitus (GDM). With its rising global prevalence, there is a pressing need for more precise, timely, and personalized strategies for diagnosis and treatment. This review highlights the role of AI in reshaping GDM management, providing actionable insights for researchers, clinicians, and policymakers. Methods: Relevant literature was identified through a non-systematic search of databases including PubMed, Scopus, and Google Scholar. Articles published up to August 2025 were considered. Studies were selected based on their relevance to the topic. Key findings were organized thematically to highlight current trends, challenges, and future directions. Results: The application of stacking-based machine learning models including random forest, AdaBoost, decision tree, support vector machine, naïve bayes, K-nearest neighbor, CatBoost, and XGBoost have facilitated the early detection of high-risk patients and achieved high precision and accuracy (88.8%) in reducing unnecessary diagnostic tests. Additionally, machine learning-based decision support systems have demonstrated promising capabilities in predicting insulin requirements and treatment failure with oral hypoglycemic drugs. The integration of AI in mobile applications and wearable devices has significantly encouraged proactive patient participation through real-time glucose monitoring and individualized feedback. Furthermore, these technologies are used to assess the long-term risk of developing type 2 diabetes postpartum, expanding the potential for innovative preventive strategies. Conclusion: While challenges such as data heterogeneity, narrow applicability, and ethical concerns persist, there is an increasing body of evidence indicating that AI can play a revolutionary role in GDM management by providing personalized, cost-efficient, and scalable solutions. Supplementary Information: The online version contains supplementary material available at 10.1007/s40200-025-01817-z.