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The process of identifying a health problem, illness, disorder, or other condition is known as disease diagnosis. Diagnosing certain diseases may be quite simple at times, but there may be more difficult cases. Large data sets are accessible, however the number of instruments that can reliably, Identify the trends and formulate hypotheses. Traditional disease diagnosis techniques involve physical labor and are prone to inaccuracy. When artificial intelligence (AI) predictive approaches are used instead of solely relying on human expertise, auto diagnosis is made possible and detection mistakes are decreased. We have examined the last ten years' worth of literature in this study, from January 2009 to December 2019. Eight of the most popular databases were examined for the study, and 105 publications in all were reviewed. A thorough examination of such publications was carried out to categorize the most popular AI methods for medical diagnostic systems. We also go over a number of illnesses and the relevant Artificial Intelligence (AI) methods, such as machine learning, fuzzy logic, and deep understanding. The purpose of this study paper is to shed light on several significant aspects of the various AI methods that are being and have been utilized in the medical profession to forecast heart disease, brain disease, prostate, liver disease, and renal disease. Finally, based on a list of unresolved issues and obstacles, the study offers several directions for future research on AI-based diagnostics systems.