Joy AbahoGodfrey MucunguziBachawa Wangolo2026-09-022026-09-022026-06-04https://hdl.handle.net/20.500.12311/3609UndergraduateType 1 diabetes is one of the most surging health condition with an approximate number of 369,100 adult cases in Uganda by 2024. There is still a lack of adaptive and personalized tools necessary to manage the disease. The GlucoSense ecosystem focuses on an integrated, full-stack with machine learning to predict insulin intakes, a nutritional system that offers meal recommendations, a meal chatbot, and glucose-guided foods for diabetic patients. The project was built on a three-tier architecture with the frontend layer developed in ReactVite for both the Clinical portal and the Meal plan User Interface, the backend layer follows a FastAPI design and the database layer was built in SQLite and SQLAlchemy. The results show that the Linear Regression model achieved the lowest error values in predicting insulin dosage with a Mean Absolute Error of 2.98, Mean Squared Error of 11.524 and a Root Mean Squared Error of 3.3947. These results proved that linear Regression was the most stable model and produced fewer dangerous miscalculations compared to random forest, gradient boosting and xgboost. However, all the models showed very low negative R2 values which would probably have occurred because of feature limitations since most features might not have been fully captured.enHealth InformaticsPredictive AnalyticsMachine LearningInsulin Dosage PredictionPersonalized HealthcareNutritional Recommendation SystemMeal PlanningDigital HealthGlucose MonitoringClinical Decision SupportInsulin Dose ClassificationExplainable AISHAPXGBoostDiabetes ManagementRetrieval Augmented GenerationFull-Stack Healthcare SystemUgandaDissertation