Electrocardiography (ECG) has long been a key diagnostic tool for detecting heart conditions. Traditionally, ECG interpretation depended heavily on trained medical professionals analyzing wave patterns manually. While effective, this approach can be time-consuming and may miss subtle abnormalities, especially during early stages of heart disease. Artificial intelligence (AI) is now transforming how ECG data is analyzed and used in healthcare. AI-powered ECG systems use machine learning algorithms trained on large datasets of heart signals. These systems can identify complex patterns, irregular rhythms, and abnormalities that may not be immediately noticeable to the human eye. By processing ECG data in real time, AI helps detect conditions such as arrhythmia, atrial fibrillation, and other cardiac irregularities earlier and more efficiently.