Chronic diseases like diabetes pose a major threat to global healthcare, with estimates suggesting a rise from 382 million cases in 2013 to 692 million by 2050. Characterized by high blood sugar and symptoms like increased thirst and hunger, diabetes can lead to serious complications if left untreated. Normally, our body converts food into fuel (glucose), and the pancreas produces insulin, a hormone that unlocks cells and allows glucose entry. There are different types of diabetes, with the most common being type 1 and type 2. In an effort to improve early detection, scientists are utilizing machine learning, a field that allows machines to learn from experience. This project explored combining various machine learning algorithms for more precise identification of early diabetic symptoms. After evaluating the accuracy of various algorithms, including Gaussian Naive Bayes Classifier (96.43%), Support Vector Machine (96.69%), Linear Regression (98.52%), and Decision Tree (98.86%), we have chosen the decision tree algorithm for its superior performance.