Abstract: Too much sugar in the body is a symptom of diabetes, a disease that is very dangerous if left uncontrolled. Diabetes complications: It can occur in many forms, including heart problems, kidney failure, high blood pressure, visual impairment, and organ damage. Early diagnosis of diabetes is important for effective management; therefore, the project will focus on using machine learning (ML) technology for accurate prediction. Using various ML classification and integration methods such as support vector machine (SVM), K nearest neighbor (KNN), decision tree (DT), logistic regression (LR), random forest (RF), and gradient boosting (GB) in the collection. We aim to increase the accuracy of facts from the patient's dataset. Each method showed different accuracy, and Random Forest emerged as the best model in our study. Analysis of the most accurate model demonstrates its ability to accurately predict blood sugar levels, leading to major advances in healthcare predictions.

| DOI: 10.17148/IARJSET.2024.11551