Owing to the profoundly irresistible nature of the SARS-CoV-2 infection, an enormous number of individuals halt in the line for CT Scan assessment, which overburdens the medical practitioners, radiologists, and adversely influences the patient's remedy, diagnosis, as well as restraint of the epidemic. Medical facilities like intensive care systems and mechanical ventilators are restrained due to highly infectious diseases. It turns out to be very imperative to characterize the patients as per their asperity levels. This article exhibited a novel execution of image segmentation and a machine learning approach for Covid-19 contamination asperity identification. With the help of the image segmentation model and machine learning classifier, we can identify and classify Covid-19 individuals into three asperity classes such as early, progressive and advanced, with an accuracy of 96% using chest CT scan image database. Experimental outcomes on an adequately enormous number of CT scan images exhibit the adequacy of the machine learning mechanism developed and recommended to identify Coronavirus severity.