The continual struggle between viral pathogens and humans has persisted throughout history. According to the theory of evolution, every organism in the world is driven to ensure its own existence, including even the smallest viruses. Thus, the transmission of infections by viruses is continuously advancing, leading to a significant impact on human health in terms of illness and death. Despite the availability of advanced technology for diagnosing, preventing, and treating infectious diseases in this period, the introduction of Diabetes Type-2 continues to pose a serious and urgent issue to the world population. An illustrative instance is the new coronavirus, COVID-19, which originated in Wuhan, China, and swiftly escalated into a worldwide epidemic. There was a lack of therapeutic options to treat the patient suffering from this emerging viral illness. During this challenging situation, doctors and pharmacists were manually prescribing the available medication based on the symptoms exhibited by the patients. Throughout this procedure, a significant number of individuals who were infected have perished as a result of inadequate medication. Thus, we have developed a disease prediction system in this study, which utilizes a range of disease signs. Furthermore, we have devised a concept that can assist the pharmaceutical industry in advancing the development of treatment for viral diseases by employing the technique of Machine Learning. This technique essentially examines the symptoms and forecasts the most appropriate medication for any novel ailment. Furthermore, this technology also predicts the necessary chemical element composition that pharmaceutical businesses can utilize to produce new medicines under the guidance of drug experts.

Type 2 diabetes, Viral Disease, Machine Learning, Anti-viral Medicine, Naïve Bayes,Decision Tree, Random Forest