Diabetes is a common chronic illness that requires ongoing patient monitoring to diagnose the condition in a timely manner. With the significant advancements of the Internet of Medical Things (IoMT) sector in recent years, it is feasible now to monitor the patient's information continuously. There are many studies that used IoMT and machine learning (ML) techniques to diagnose diabetes but so far, the accuracy of the performance is still below the required level. Therefore, this study proposes a common framework for IoMT, cloud, and ML techniques to diagnose diabetes in real-time. IoMT devices continuously collect vital information of diabetic patients such as glucose and insulin levels. Then, this data is transmitted using various communication technologies to be stored in the cloud for diagnosis. Finally, to improve diagnostic accuracy, voting ensemble strategy-based method has been proposed that combines predictions from three base ML techniques (Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF)). The proposed voting model achieved promising results in diagnosing diabetes with an accurate rate of up to 98.0%, outperforming the base classifiers in this and previous studies.