Electrocardiography (ECG) remains one of the most widely used, non-invasive diagnostic tools in cardiovascular medicine. Accurate ECG interpretation is critical for the diagnosis and management of arrhythmias, myocardial infarction, conduction abnormalities, and other cardiac conditions. However, interpretation errors and variability among clinicians continue to present challenges in clinical practice. Recent advancements in Artificial Intelligence (AI), particularly machine learning and deep learning, have revolutionized ECG analysis by enabling rapid, accurate, and automated interpretation of cardiac electrical activity. AI-assisted ECG systems can detect subtle abnormalities, predict cardiovascular events, and support clinical decision-making with high precision. Despite these opportunities, concerns regarding data quality, algorithm transparency, clinical validation, ethical considerations, and regulatory compliance remain significant barriers to widespread adoption. This study reviews the role of AI-assisted ECG interpretation in modern cardiac care, examines current applications, evaluates benefits and challenges, and discusses future directions for integrating AI into cardiovascular diagnostics. The findings suggest that AI has substantial potential to enhance diagnostic accuracy, improve patient outcomes, and optimize healthcare efficiency when implemented responsibly alongside human experience.