AI is utilized in different regions like instruction and medical services. With the progression of innovation, the better registering power and accessibility of datasets on open-source vaults have additionally expanded the utilization of AI. The medical services area creates a lot of information as far as pictures, patient information, etc. that assists with distinguishing examples and make forecasts. Coronary illness depends on the individual, and the degree of coronary illness can differ from one individual to another. Along these lines, making an AI model, preparing it on the dataset, and entering individual patient subtleties can help in expectation. The forecast outcome will be as per the information entered and subsequently will be explicit to that person. Diabetes is an infection that can be forestalled by control of weight, way of life, etc. Bosom malignant growth basically influences ladies (with < 1% of cases influencing non-females); approximately one of every eight ladies foster bosom disease in the course of their life. Thusly, the early determination of bosom disease is fundamental to great guess. Notwithstanding the way that the side effects might be powerless in the beginning phases, chances of endurance drastically increment whenever identified early. Issues with liver patients are not effectively found in a beginning phase as it will be working regularly in any event, when it is to some degree harmed. An early finding of liver issues will expand patient's endurance rate. Liver disappointments are at high pace of hazard among Indians. It is normal that by 2025 India might turn into the World Capital for Liver Diseases. Constant Kidney Disease (CKD), i.e., continuous diminishing in the renal capacity traversing over a length of a while to years with next to no significant side effects, is a hazardous sickness. Early location and fix of CKD is incredibly alluring as it can prompt the anticipation of undesirable results. AI strategies are as a rule broadly pushed for early discovery of side effects and analysis of a few illnesses as of late.

The main contributions are as follows:
I.	An efficient automated disease diagnosis model is designed using the machine learning models.
II.	Few critical diseases are selected such as Liver disease, heart disease, beast cancer, kidney disease and diabetes.
III.	In the proposed model, the data are entered into the web app, the analysis is then performed in a real-time database using a pre-trained machine learning model which was trained on the same dataset and deployed in firebase, and finally, the disease detection result is shown.
IV.	Random Forest is used to carry out computation for prediction.