Currently, the diagnosis and monitoring of diabetes mellitus (DM) primarily rely on clinical symptoms, blood glucose measurements, and laboratory blood tests. With the continuous advancement of medical imaging, magnetic resonance imaging (MRI) is increasingly being applied to the study of diabetes and its associated complications. Given that the pancreas plays a pivotal role in the pathogenesis and progression of DM, quantitative MRI techniques have emerged as powerful tools; they provide not only fundamental structural information but also visualize the pathophysiological alterations of the pancreas throughout the disease course. This literature review suggests that quantitative pancreatic MRI holds great promise as a novel, non-invasive biomarker for evaluating diabetic pathophysiology, facilitating early diagnosis, and monitoring therapeutic efficacy. Advancing this field further, the integration of artificial intelligence and radiomics offers powerful tools for automated high-throughput mining of these datasets, though its clinical translation remains preliminary. Current implementation gaps-most notably the critical need for multi-center external validation and standardized models-must be recognized before these deep imaging phenotypes can be reliably utilized in clinical settings.