An electrocardiogram, or ECG, is one of the most important diagnostic and monitoring devices used for cardiovascular health. In one sense, it provides an appreciation of the heart’s rhythm and function that enables the diagnosis of a large variety of cardiac conditions. However, manual interpretation is a common component of traditional methods of ECG analysis techniques, and it may be laborious, unreliable, and prone to human mistakes. This research proposes an automated and enhanced technique for ECG heartbeat detection using machine learning to overcome the complexities. Our technique attempts to classify heartbeats as normal or abnormal with good precision by developing a sophisticated model that scrutinizes significant ECG signal characteristics, like waveform duration, amplitude, and morphology. Considering the presence of an extensive dataset of ECG recordings with which the developed model will be trained as well as validated, the above-mentioned model robustness and generalizability are ensured in advance. With the aspiration to enhance early cardiac abnormality detection, this automated system offers doctors a reliable and effective decision. Using this model would lead to a complete change in the cardiovascular diagnostics process when incorporated into medical procedures, because it will be able to provide ongoing, safe monitoring that will eventually improve patient outcomes while saving costs for healthcare.