With the ongoing development of technology, it is becoming more and more crucial to predict the signs of numerous diseases. This will enable lay people and inexperienced physicians to form accurate opinions about the disorders. Therefore, in this case, we will employ a machine learning technique that determines if the patient has multiple diseases or not based on the symptoms of historical data. The current system generates the projected output by analyzing a single disease based on its symptoms and employing Techniques like Decision Tree, K-Nearest Neighbors and Naïve Bayes which might produce erroneous results when forecasting diseases. When compared to other algorithms, these are the algorithms with the lowest accuracy. In this proposed system, we'll use Machine Learning algorithms like Random Forest and Support Vector Machine (SVM) to predict diseases at once based on different symptoms. The result is outdated if it matches the related symptoms that predict the related disease, and the techniques we used are more accurate than previous findings with accuracy of 97.86%.