Diabetes mellitus is a growing chronic health condition that needs to be detected at an early stage to avoid complications. Machine learning (ML) is proving to be an efficient solution for developing Clinical Decision Support Systems (CDSS), which aid doctors in diagnosing and predicting diseases. The research aims to develop an artificial intelligence-based CDSS for diabetes prediction using supervised machine learning algorithms such as Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB). The results of the experiments prove that the ensemble methods are better than traditional methods. The proposed CDSS is a solution for predicting diabetes mellitus and shows its potential for providing accurate insights for decision-making in the health care industry. The use of such artificial intelligence-based CDSS is significant for decision-making in health care.