Medical assistance as a form of automated software that uses computer technology and machine learning methods. Recently it is being developed to help detect cardiac illness at an early stage. The risk of mortality may be reduced if any heart-related sickness is discovered at an early stage. Medical data may be analysed using a variety of machine learning (ML) approaches. The sheer amount and complexity of healthcare data makes it difficult to make sense of it. ML algorithms are able to process large amounts of data and extract relevant information from it. Algorithms for machine learning make predictions based on historical data and learn from it. Using a machine learning framework like this to predict the onset of coronary heart disease may motivate cardiologists to act more quickly, allowing more patients to get life-saving medications more quickly. This paper mainly explores the heart prediction through ML learning algorithms and its mathematical analysis. First this research illustrated the cardio disease in theoretical manner and apply the few selective machine learning for the implementation of the cardio data (Taken from UCI Library) in MATLAB platform. This paper found that the MLP is the best predictor algorithm and suitable for cardio data analysis and modelling.