In medical practise, an electrocardiogram (ECG) is a crucial indicator tool for assessing cardiovascular arrhythmias. In this study, a machine learning system is used to compare patient ECGs and perform programmed ECG arrhythmia identification. The system was previously tuned based on an overall image informational index. Arrhythmias are more prevalent in those over the age of 60. A convolutional neural network (particularly, Alex Net) is utilised to extract features, and the highlights are then passed via a basic back spread neural network to finish the classification. The fundamental purpose of this research is to provide a simple, effective, and relevant learning strategy for categorising the three types of heart conditions (cardiac defects) so that a diagnosis may be made. The findings showed that when a moving deep learning highlight extractor was combined with a standard back proliferation neural architecture, very elite rates could be achieved. In a comparative analysis, validation accuracy was shown to be 100 percent in Google Net, 94 percent in Squeeze Net, and about 97.33 percent in Alex Net