In recent years, interventional cardiology, particularly structural heart interventions, has advanced rapidly with continual updates in both devices and imaging technologies. The entire workflow, including pre-procedural assessment, intra-procedural real-time monitoring, and post-procedural follow-up, heavily relies on echocardiography. However, traditional ultrasound examinations are susceptible to limitations such as image quality, consistency of cross-sectional acquisition and operator experience. These restrict measurement repeatability and real-time decision-making efficiency. Artificial intelligence (AI), especially deep learning and integrated with echocardiographic image analysis, has shown significant potential in standardized acquisition of ultrasound images, identification of cross-sections and fine structures, automatic quantification of function and hemodynamics, perioperative risk stratification and early warning of complications. When powerful advantages of cardiovascular ultrasound AI are fully leveraged, it is expected to further promote the development of precision and efficiency in interventional cardiac treatment. This article starts with the basic principles and methods of ultrasound AI, systematically reviews its key application progress in the entire process of cardiac interventional surgery. It looks forward to the future development trend of interventional echocardiography AI. The purpose is to provide a reference for clinical research and translation.