Diabetes is a chronic condition that has the potential to wreak havoc on the global healthcare system. Diabetes mellitus, or just diabetes, is a condition characterized by a rise in blood glucose levels. Diagnosing diabetes can be done using a variety of traditional approaches based on physical and chemical testing. However, due to the complicated interdependence of different elements and the fact that diabetes affects human organs such as the kidney, eye, heart, nerves, and foot, early diabetes prediction is challenging for medical practitioners. Machine learning is a new discipline of data science that studies how machines learn from their past experiences. The goal of this research is to create a system that can combine the findings of several machine learning approaches to conduct early diabetes prediction and advice for a patient with greater accuracy. The goal of this study was to create a system based on three classification algorithms such as Decision Tree, Naive Bayes, and Support Vector Machine. In comparison to an individual classifier, classification techniques are widely employed in the medical sector for categorizing data into distinct classes based on specified constraints. Diabetes is a disease that impairs the capacity of the body to produce the hormone insulin, which causes carbohydrate metabolism to become abnormal and blood glucose levels to rise. High blood sugar is the most common symptom of diabetes. Among other symptoms, high blood sugar can induce increased thirst, hunger, and frequent urination, among other symptoms. If diabetes is not managed, it can lead to various problems. Diabetic ketoacidosis and nonketotic hyperosmolar coma are two of the most serious consequences. Diabetes is considered a major health problem in which the amount of sugar in the blood cannot be regulated. Diabetes is influenced by various factors such as height, weight, genetic factors, and insulin, but the most important aspect to consider is sugar concentration. The best way to avoid difficulties is to detect the problem at an early stage.