In the domain of healthcare and well-being, the fusion of machine learning and data collection through medical examinations has propelled significant advancements in diabetes monitoring. Diabetes, a prevalent and intricate health condition, has garnered increasing attention due to its substantial impact on individuals’ mental and physical well-being. The use of medical examinations for real-time diabetes assessment has become pivotal, with various physiological monitoring capabilities aiding in this endeavor. Machine learning, as a subset of artificial intelligence, has further elevated the precision and effectiveness of diabetes monitoring by extracting meaningful insights from the extensive and intricate data collected through these examinations. This paper introduces a novel and interpretable computational model known as the Evolving Fuzzy Neural Network Uni-Nullneuron-Based Approach (EFNN-UniNull). Comprising three interconnected layers, this model collaboratively produces classification outcomes while concurrently providing insightful interpretations of relationships within the age group identification of diabetes patients dataset. The fuzzification method based on grid partition helps in obtaining adequate knowledge about the problem. The model underwent a comparative analysis against evolving neuro-fuzzy systems, demonstrating results approaching 85% accuracy. Notably, the model extracted knowledge based on fuzzy rules pertinent to diabetes identification.