Diabetes Mellitus is a public health challenge with social and economic consequences for nations irrespective of their economic stability. The Ministry of Health reports that diabetes is on the rise for Omanis and has increased to the extent that it is the second ranked most prevalent disease among non-communicable diseases, a significant cause of health loss condition in Oman. Despite this, diabetes is preventable by accepting a healthier lifestyle. Thus, an appropriate tool for prognosis can assist the doctors in detecting the disease early and recommending the necessary lifestyle changes to reduce its progress. Information overloaded to the Clinic and health care centres for the patients that require sophisticated analysis tools and techniques to extract valuable knowledge and transform them into understandable patterns for decision-making. The latest data mining techniques, including ensemble algorithms, can be applied to significantly obtain the hidden patterns from the voluminous dataset to improve the diagnosis of diabetes at the initial stage of pre-diabetes. This research aims to construct a predictive ensemble model that can significantly classify and predict diabetes at the earliest stage accurately using the dataset collected from the Sultan Qaboos University Hospital database from Jan 2019 to October 2021. The ensemble model overcomes the variance problem of individual algorithms by combining the prediction results from different algorithms to create strong prediction results. The study applied five supervised classification algorithms to build the Stacking and Voting ensemble models, including Random Forest (RF), Decision Tree (J48), K-Nearest Neighbours (KNN), Support Vector Machine (SVM), and Naïve Bayes (NB). These algorithms were evaluated using different performance evaluation metrics, including recall, accuracy, AUC curve and precision, and validated using the cross-validations process to overcome the bias of the individual algorithms. The outperform algorithm is then used to demonstrate the utilisation of the model and discover the hidden knowledge in the dataset. The study outcomes confirm that accuracy achieved by the ensemble algorithms, including stacking, and voting models, produce promising results compared to other techniques for learning from complex datasets due to combining different prediction results from the individual algorithms. Because the stacking algorithm combines the correct prediction results from the individual classifiers, it provides the most accurate results of 86.9% and an AUC curve of 86.9% in predicting diabetes. Therefore, it can be used as an automatic prognostic tool to improve the diagnosis system in healthcare. However, it can be optimised further to progress prediction accuracy by including the significant risk factors in predicting diabetes. An improved diabetes diagnosis system using machine learning methodologies results in the awareness and accelerating decision-making that may help physicians and doctors understand the disease better and enhance proper treatment. Consequently, it reduces hospitalisations, improves clinical outcomes, and minimises health expenditure for government and individuals.