<p>Artificial intelligence (AI) is rapidly transforming the field of echocardiography. By leveraging machine learning, particularly deep learning, AI enhances image acquisition, interpretation, and diagnostic accuracy. It addresses long-standing limitations of echocardiography, such as operator dependency and inter-observer variability. AI-enabled systems, ranging from probe guidance to automated quantification tools, have improved image quality and reduced variability in key measurements such as left ventricular ejection fraction (LVEF). Recent studies show that AI can assist in disease classification, detect regional wall motion abnormalities, and predict disease progression with accuracy comparable to expert assessment. Despite these advances, several challenges remain. Concerns regarding data bias, limited generalizability across populations and devices, and the “black-box” nature of many AI models hinder clinical adoption. Ethical issues, including data privacy and unequal access to digital technologies, also require careful attention. Importantly, AI should be viewed not as a replacement for human expertise but as a tool to augment clinical decision-making and improve workflow efficiency. Looking ahead, integrating echocardiographic data with other clinical information through AI could enable earlier diagnosis and better patient management. As technology evolves, AI is expected to reinforce echocardiography’s role as a non-invasive, widely available, and highly informative diagnostic modality. Continued research and rigorous validation are essential to ensure the safe, equitable, and effective use of AI in clinical echocardiography.</p>