Electrocardiography (ECG) remains a cornerstone of non-invasive cardiac diagnostics, yet manual interpretation poses challenges due to its complexity and time consumption. The integration of Artificial Intelligence (AI), particularly through Deep Learning (DL) models, has revolutionized ECG analysis by enabling automated, high-precision diagnostics. This review highlights the recent advancements in AI-driven ECG applications, focusing on arrhythmia detection, abnormal beat classification, and the prediction of structural heart diseases. AI algorithms, especially convolutional neural networks (CNNs), have demonstrated superior accuracy compared to human experts in several studies, achieving precise classification of ECG patterns across multiple diagnostic categories. Despite the promise, real-world implementation faces challenges, including model interpretability, data privacy concerns, and the need for diversified training datasets. Addressing these challenges through ongoing research will be crucial to fully realize AI's potential in enhancing clinical workflows and personalizing cardiac care. AI-driven ECG systems are poised to significantly advance the accuracy, efficiency, and scalability of cardiac diagnostics.