Machine learning analysis of pancreatic tissue from living donors revealed that alpha and delta cells, combined with neuronal axons, provide the strongest predictive signals for type 2 diabetes status. The AI model achieved optimal performance by simultaneously analyzing these cellular components alongside subtle tissue changes including enlarged fat cell clusters, altered spatial relationships between islets and adipocytes, and reduced islet size. This finding challenges the conventional focus on beta cells as the primary diagnostic marker for diabetes progression. The computational approach overcomes human analytical limitations when processing gigapixel microscopy data that captures morphological changes too subtle for traditional histopathological assessment. The identification of alpha cells and neural tissue as key predictive features represents a significant shift in understanding diabetes pathophysiology at the tissue level. While most diabetes research emphasizes beta cell dysfunction and insulin secretion failure, this data-driven discovery suggests that non-beta islet cells and pancreatic innervation play equally critical roles in disease development. The approach provides a scalable framework for identifying diagnostic biomarkers that could inform earlier intervention strategies, though validation across diverse populations and correlation with functional glucose metabolism remains necessary before clinical implementation.