The urgent global problem of cardiovascular diseases is addressed in this study, with a special emphasis on heart disorders, which continue to be the world's leading cause of mortality. Early diagnosis is essential, and because electrocardiograms (ECGs) are non-invasive and reasonably priced, they are an essential tool for heart health monitoring. This study uses sophisticated learning methods; transfer learning methods like SqueezeNet and AlexNet, along with customized Convolutional Neural Network (CNN) primarily used to increase prediction accuracy. The four major cardiac abnormalities that these techniques seek to identify are unusual heartbeat, myocardial infarction, if any history of myocardial infarction, and any normal instances. This model's remarkable performance is what makes it unique; it is attained by combining deep learning and conventional machine learning methods to extract important information. This study demonstrates how artificial intelligence can revolutionize the healthcare industry, particularly in the area of medical condition prediction via picture analysis.