Artificial intelligence (AI) machine learning (ML) branch has been actually utilized in the clinical field to analyze sicknesses. As well as diagnosing normal illnesses, machine learning calculations have exhibited comparative capability in diagnosing extraordinary sicknesses. While machine learning (ML) gives deliberate and refined calculations to multi-layered clinical information, its precision in disease finding stays an issue of discussion. We really want a method for applying different best in class calculations with ideal lines of code since various machine learning strategies perform contrastingly for various medical services datasets. This will permit us to look for the best machine learning technique to really identify a specific infirmity more. In this review, that's what we show, with a couple of lines of code, one might assess the exhibitions of a few machine learning calculations to disease finding for a given dataset by utilizing libraries like AutoGluon. This will decrease the probability of an inaccurate conclusion, which is a vital element to consider while dealing with individuals' wellbeing. Involving the Maharashtra Diabetes Dataset as a premise, we assessed the viability of twenty machine learning procedures.