Deep learning has emerged as a transformative tool in medical diagnostics, offering the ability to analyze complex biomedical signals with remarkable accuracy and efficiency. In the context of cardiovascular diseases (CVDs)—a leading global health burden early detection is crucial to prevent severe complications and reduce mortality. ECG interpretation with manual analysis and expert judgment produces results that take excessive time and tend to result in errors. This study proposes a deep learning-based approach for automated ECG analysis to support early diagnosis and classification of cardiac conditions. The model establishes four distinct categories called Healthy and Abnormal Heartbeat (AH) and Myocardial Infarction(MI) and History of Myocardial Infarction(HMI). Automatic extraction of major features from ECG signals by deep learning helps the system to improve its accuracy and avoid demanding hand-crafted features. A variety of deep learning models are used and then compared to find the one that works best for strong classification. Improved and quicker diagnosis is possible because of the system, which is beneficial for clinical support. Based on this study, AI-powered systems can work with standard techniques to provide improved patient health outcomes and a smoother journey in cardiology.