Artificial Intelligence in Diabetes Mellitus Management: A Narrative Review of Advances, Architectures, and Translational Challenges

INTRODUCTION: Diabetes Mellitus (DM), a chronic disorder caused by insulin deficiency, dysfunction, or both, is projected to affect 640 million people by 2040. Traditional management is limited by drug resistance, poor adherence, and insufficient personalization. This review examines Artificial Intelligence (AI) applications addressing these gaps in DM care. METHODS: PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar were searched (January 2020-December 2025) using combinations of diabetes mellitus, artificial intelligence, machine learning, deep learning, federated learning, explainable AI, and blockchain. Studies addressing AI-based risk prediction, glucose forecasting, complication detection, and secure or transparent frameworks were included, reported by diabetes subtype where relevant. RESULTS: Convolutional neural networks, long short-term memory networks, support vector machines, and transformer architectures have improved glucose forecasting, complication screening, and risk prediction. Federated learning enables privacy-preserving multi-institutional training, while SHAP and LIME improve interpretability, and blockchain offers a tamper-proof audit layer. However, most high-performing models remain internally validated, limiting generalizability. DISCUSSION: Unlike prior reviews that address a single application domain or treat T1DM and T2DM as a single population, this review is organized around their distinct AI design needs and classifies applications as experimental, commercial, or regulator-approved. AI can extend beyond technical accuracy by integrating diverse clinical data, though explainability and validation remain central to adoption. CONCLUSION: AI has the potential to transform DM care through more precise forecasting, earlier complication detection, and individualized treatment. Realizing this requires stronger regulatory frameworks, external validation, reduced demographic bias, and improved interpretability, prioritized through prospective multicenter studies, standardized datasets, and explainability standards embedded in clinical workflows.