The digitalization of health informatics is revolutionizing the discipline of medicine. The advancements to extract knowledge from complex clinical digitized data have led to significant developments in health care. Machine Learning techniques can infer medically actionable knowledge that will support doctors and health care stakeholders to deduce the best possible medical decisions. Objective: To evaluate the applicability of the Machine Learning model in diagnosing the disease. Methods: A systematic review and literature survey to understand and elaborate the significant impacts on the prognosis, detection, and diagnosis of diseases by using various Machine Learning techniques is carried out. Result: Various Machine Learning models have been effectively been used in recent years for classifying patients and normal. A combination of bagging and boosting techniques can foster results and open newer avenues of accurate predictions. Conclusion: A thorough study of the various research undertaken in the domain of computational medicine, it was speculated that Machine Learning models are useful in applications for disease diagnosis involving complex clinical data.