The phrase “diabetes mellitus” refers to a range of conditions affecting the brain’s regulation of glucose levels, also known as blood sugar. The neurons within the tissues and muscles rely on sugar as their primary energy source. The brain processes it to be its principal source of energy. At present, there exists no enduring remedy for diabetes, while effective earlier detection has been achievable, the higher risk and intensity of Mellitus can be greatly diminished. As per the significant spike in illness, the number of diabetics worldwide would be expected to exceed 6,42,000 K by 2040, or one out of every ten persons. The major goal of this paper is really to create a framework for such premature detection of diabetes by employing different kinds of machine learning classification techniques while taking important diabetes-related factors into account. For all this, we employed supervised machine learning approaches such as logistic regression, K-nearest neighbors, gradient-boosting classifier, decision tree classifier, AdaBoost classifier, SVM—linear kernel and naive Bayes. After employing each of the healthcare data, we had capability of building the most superior machine learning model, i.e., logistic regression with 76% accuracy. We employed another model by combining machine learning classifiers, namely, GridSearch, LightGBM as well as K-nearest neighbors, and achieved the accuracy of 90%, which properly predicts how many of the individuals in the database possess diabetes. We have also been capable of extracting some conclusions from the information through data processing and analysis.