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Diabetes mellitus is a fast growing universal public health concern that greatly increases sickness, mortality, and economic burden, especially in low- and middle-income areas like South Asian nations. The rising incidence of diabetes and the shortcomings of traditional healthcare delivery methods accentuate the pressing need for creative, scalable, and easily accessible alternatives. The growing role of artificial intelligence (AI) and machine learning (ML) in the diabetes care weighbridge including early risk prediction, diagnosis, individualized treatment, real-time monitoring, and complication prevention is studied in this paper. To evaluate worldwide and SAARC specific changes in diabetes prevalence and estimated disease burden, a secondary data based comparative study was carried out using epidemiological data from the International Diabetes Federation (IDF) Diabetes Atlas. Concurrently, a comprehensive review of current AI and ML-driven diabetes management applications was conducted, with a focus on predictive modeling, AI assisted screening, and new glucose monitoring technologies like continuous glucose monitoring and flash glucose monitoring. The results show that the prevalence of diabetes is suspiciously high and rising quickly among SAARC countries. Models based on AI and machine learning show great promise for early detection, better glycemic control, better drug adherence, and prompt identification of problems associated with diabetes, particularly diabetic retinopathy.