Hospitals are the most common option for health checks, illness diagnosis, and treatment for sick people. This practice is followed by almost everyone in the world. But there is a drawback with this method of getting diagnosed. There are a lot of patients with various diseases/viruses which have a potential to spread in the hospital premises. People never considered the diseases/viruses present in the hospital atmosphere. People are aware of the risk of viral transmissions in hospital environments, post COVID era. Getting diagnosed and going through the reports with an efficient accuracy takes time and some people in emergency may not have enough time to perform the conventional procedures. Users have a necessity of an online website which can help them diagnose their health problems at the comfort of their homes. This would benefit people as they don’t have to travel to the hospitals and reduce their risks of transmitting hospital acquired infections. This paper presents an interactive interface that functions as a virtual therapist which accepts input in the form of text, voice, or video. Data is pushed into the machine learning pipeline that generates results. The end result of this model is a report containing root cause of the disease, a tentative prescription, and any estimated treatment expenses. This model helps to prevent hospital-acquired infections, reduces the costs of treatment as users would be able to diagnose earlier and would prefer frequent testing, reducing surgeries and also reduces the tasks of doctors.