Machine Learning is an intelligent methodology used for prediction and has shown promising results in predictive classifications. One of the critical areas in which machine learning can save lives is diabetes prediction. Diabetes is a chronic disease and one of the 10 causes of death worldwide. It is expected that the total number of diabetes will be 700 million in 2045; a 51.18% increase compared to 2019. These are alarming figures, and therefore, it becomes an emergency to provide an accurate diabetes prediction. Health professionals and stakeholders are striving for classification models to support the prognosis of diabetes and formulate strategies for prevention. This paper provides a novel intelligent diabetes mellitus prediction framework (IDMPF) using machine learning. The framework is the result of a critical examination of prediction models in the literature and their application to diabetes. We identify the training methodologies, models’ evaluation strategies, the challenges in diabetes prediction and propose solutions within the framework. We implement and evaluate the Decision Tree-based Random Forest and the Support Vector machine learning models for diabetes prediction using our framework as they are the two most used approaches. Our research results can be used by health professionals, stakeholders, and researchers working in the diabetes prediction area.