The potential of Machine Learning for the healthcare sector is immense. Several Machine Learning techniques have been used to perform predictive analytics in various fields. Predictive analytics in healthcare can help doctors to make decisions about patient’s health and treatment. In addition, the algorithmic approach can be easily used at the scale of a very large number of patients. The proposed project, integrated into this area, aims to propose a solution for diabetes prediction based on Machine Learning techniques to help doctors in early prediction of diabetes and reduce the hospitalizations related to diabetes disease. In this project, we have proposed a diabetes prediction model using Random Forest (RF) classifier that has shown comparatively high performance. To validate our model, we have make experimental evaluation using Diabetes dataset. We compared RF with Naïve Bayes (NB) classifier using the accuracy metrics. The comparative evaluation have shown interesting results. In addition, we have implemented a Graphical User Interface (GUI) for the proposed model to help doctors to take benefit of the model.