Modeling strategies for CGM data: A scoping review of mechanistic, machine learning, and hybrid approaches in diabetes management

Background The increasing availability of continuous glucose monitoring (CGM) data has opened new avenues for modeling glucose dynamics in diabetes management. Objective This scoping literature review uniquely explores the full methodological spectrum applied to CGM data analysis, ranging from classical Mechanistic Models (MM) and statistical time series approaches, to modern Artificial Intelligence (AI) techniques, and emerging hybrid frameworks that combine the two paradigms. Unlike prior reviews focused solely on Type 1 Diabetes Mellitus (T1DM), the present work includes modeling efforts across different classes of populations, including Type 2 Diabetes Mellitus (T2DM), Gestational Diabetes Mellitus (GDM), and other forms of diabetes. Methods Literature was systematically retrieved from Elsevier Scopus®, Clarivate Web of Science™, and PubMed®, providing a comprehensive and comparative assessment of state-of-the-art strategies for CGM-based analysis in diverse clinical contexts. Results The reviewed studies indicate a clear methodological shift toward data-driven and hybrid frameworks. Overall, both mechanistic and AI-based models achieve satisfactory predictive performance; however, their strengths differ across tasks. Machine learning methods are particularly effective for event detection and feature extraction, whereas deep learning models excel in forecasting CGM-derived glucose trajectories. Hybrid approaches further enhance predictive accuracy while preserving physiological interpretability, especially over longer prediction horizons. Conclusion Challenges such as data heterogeneity, the limited availability of high-quality datasets beyond T1DM, and reduced cross-cohort generalizability persist, underscoring the need for standardized validation procedures and physiologically informed modeling strategies.