The electrocardiogram (ECG) is a fundamental tool for diagnosing a wide range of cardiac conditions. The application of artificial intelligence (AI) to ECG analysis has shown significant potential in improving diagnostic accuracy and efficiency. This review provides a comprehensive overview of the current state of AI in ECG recognition, exploring the methodologies, applications, challenges, and future directions of this rapidly evolving field. We delve into the seminal research papers that have shaped the landscape of AI-enhanced ECG, discuss the various machine learning and deep learning techniques employed, and highlight the diverse applications of AI in diagnosing different cardiac diseases. Furthermore, we examine the performance evaluation metrics used, the current challenges and limitations, the crucial role of data preprocessing and feature engineering, and the perspectives of clinicians who utilize and evaluate AI in ECG recognition. This review aims to offer valuable insights for researchers, clinicians, and healthcare professionals interested in the intersection of AI and cardiology.