Diabetes, a prevalent chronic disease, poses significant challenges in healthcare management worldwide. This study presents a comprehensive analysis of a dataset comprising 1,000,000 instances with 12 features and a prediction variable that indicates the presence or absence of type 1,2 and 3 diabetic diagnoses in a different age group patient depending on weight factors, family history, and other genetic factors. This research deployed sophisticated data mining techniques and machine learning algorithms to help in understanding potential risk factors and predictors of the disease by examining the intricate interplay among demographic, clinical, and lifestyle variables. This dissertation also investigates the deep analysis of type 1, type 2, and type 3 medicines that are being used for different age groups of patients (children, young, and older). This study represents a significant contribution to diabetes research, offering valuable insights into the complex dynamics of the disease. The significance of data mining tools and prediction algorithms, the research endeavors to advance our understanding of diabetes etiology, risk assessment, and management strategies, ultimately contributing to improved patient outcomes and healthcare decision-making.