Data science and machine learning have evolved along with modern technology, paving the way for healthcare organisations and medical facilities to better serve their patients by assisting in the early detection of diseases. Massive volumes of data, some of which are concealed, are gathered by the health care sectors and are used for making informed judgments. We are putting forth a system for diagnosing diseases including chronic kidney disease, liver disease, heart disease, breast cancer and diabetes so they may be treated early on and the user can learn more about them. Medical diagnoses necessitate visiting a doctor, making an appointment for a consultation, and waiting for blood results to seek a doctor’s consultation in order to obtain correct disease indicators. When we don't feel well, the first thing we do is examine our temperature to get a rough estimate or baseline idea of how fevered we are. If the temperature is high enough, we then consult a doctor. In a similar way, this disease prediction system can be used to determine the disease's approximate severity and can advise us as to whether we should seek emergency medical assistance or not, or at the very least begin some home cures for the condition to provide temporary comfort. The Disease Prediction approach, which focuses on predictive modeling, makes disease predictions for users based on the symptoms they supply as input. As an output, the method returns the likelihood of the condition after analysing the user's symptoms as input.