Ch.Niharika, A.Hima Bindu, A.Mounika, G.Kiranmai 03 4 Diabetes is a chronic disease with the potential to cause a worldwide health care crisis According to International Diabetes Federation 382 million people are living with diabetes across the whole world By 2035 this will be doubled as 592 million COVID19 pandemic has rapidly affected our daytoday life disrupting the world trade and movements In this situation diabetic patient standing in the queue is a critical issue Diabetes mellitus or simply diabetes is a disease caused due to the increase level of blood glucose Various traditional methods based on physical and chemical testsare available for diagnosing diabetes However early prediction of diabetes is quite challenging task for medical practitioners due to complex interdependence on various factors as diabetes affects human organs such as kidney eye heartnerves foot etc Data science methods have the potential to benefit other scientific fields by shedding new light on common questions One such task is to help make predictions on medical data Machine learning is an emerging scientific field indata science dealing with the ways in which machines learn from experience The aim of this project is to develop a system which can perform early prediction of diabetes for a patient with a higher accuracy by combining the results of different machine learning techniques This project aims to predict diabetes via four different supervised machine learning methods including KNN Logistic regression Decision Tree Random Forest This project also aims to propose an effective technique for earlier detection of the diabetes disease Copyright (c) World Journal of Pharmaceutical Seiences. All rights reserved 2026