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Diabetes mellitus is a chronic metabolic disorder in which the level of blood glucose is high due to inadequate insulin production or improper use of insulin. It has turned out to be one of the most serious challenges facing the world today, affecting millions of people. The early detection and proper classification of diabetes is very important in order to avoid serious health problems, which may arise in the future. The conventional systems used in the detection of diabetes are not only time-consuming and costly, but also only provide binary classification, which can only identify whether a person is diabetic or not. In recent times, the integration of Artificial Intelligence (AI) and Machine Learning (ML) in the field of healthcare has led to the creation of smart systems in the detection of diseases. Machine learning has been found to be very effective in the prediction of diseases, as it can identify hidden patterns in the data. Despite the creation of smart systems in the prediction of diabetes, many of the existing systems only provide binary classification, which does not provide in-depth information on the severity of the disease. The proposed system in this research aims to develop an AI-Based Diabetes Severity Prediction Web Application that can classify diabetes into different stages using a multi-class classification algorithm. The proposed system uses a Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel to perform classification. Moreover, it uses different feature selection methods like Elephant Herding Optimization (EHO) to improve the accuracy and efficiency of the model. The proposed system uses different medical parameters like glucose level, blood pressure, BMI, insulin levels, and age to perform classification. The parameters are retrieved using a web interface. The data is then normalized and scaled to perform classification using the proposed model. The proposed model can classify diabetes into different classes like Normal, Pre-Diabetes, Type 2 Diabetic, and Severe Diabetic. The proposed system provides different advantages to users. The proposed system can provide better accuracy in prediction, can perform classification in real-time, and can provide an interface to users that is easy to use. Moreover, the proposed system can be accessible to users via web platforms to provide better health care to patients suffering from diabetes.