The Istetab initiative integrates advanced mathematical modeling and machine learning (ML) to democratize predictive health analytics. This work demonstrates a scalable ML framework focusing on early diabetes detection, utilizing XGBoost and Optuna for model refinement. Leveraging a robust and diverse dataset of patient demographics and health indicators, the model achieved an AUC score of 0.95, precision of 0.97 and a prediction accuracy of 95%. Istetab underscores the transformative potential of ML in healthcare, providing accessible, low-cost solutions, particularly for developing nations. Future directions include expanding the framework to predict other diseases and fostering integration into clinical workflows to enhance patient care globally.