COVID-19 is a highly transmittable viral infection with symptoms similar to some common diseases like malaria, diabetes, asthma and viral pneumonia. The global spread of the disease has made government of different nations to put measures in place to curtail its spread within their territories. Critical issues were raised as a result of these measures especially with mode of prevention, diagnosis and treatment. There is currently no cure found yet for the virus but health administrators isolate suspected COVID-19 patients and treat with various combinations of drugs and therapy. The confusing symptomatic resemblance with other diseases makes it extremely difficult to precisely diagnose its infection in patients. The high rate of spreading also made it imperative to isolate any person presenting any of the symptoms listed as COVID-19 patients; therefore, mistakes are bound to be made with this nonspecific diagnosis. This paper is proffering a technological way of resolving these critical issues by the application of Machine Learning, an Artificial Intelligent tool in the diagnosis of the disease. Python is herein used to build a system in pytorch and jupyter environment. The system successfully separated and classified the critical symptoms from general and common illnesses before following up on other tests while crossreferencing with laboratory and scan test results in the database, all with monitored consideration of the similarities in their symptoms