Purpose of the Review: This review discusses the recent advances in automated echocardiography using artificial intelligence and machine learning (ML) techniques. Specific emphasis is placed on the potential for machine learning-based methods to improve accuracy and reproducibility of echocardiographic assessment as well as early cardiovascular disease detection and personalized risk assessment. Recent Findings: Echocardiography remains the first line imaging modality for evaluation of many cardiovascular diseases. The last few years have witnessed a rapid expansion and growth of ML-based automated analysis and interpretation of echocardiography. These ML algorithms have shown great promise for improving data reliability, accuracy, and reproducibility of echocardiographic results. We anticipate that the application of ML algorithms will further expand the indications of echocardiography to include diseases that are traditionally only diagnosed with the more advanced imaging modalities such as cardiac magnetic resonance imaging. The ability to leverage ML’s robust capability for processing large and complex datasets will result in improved diagnosis of cardiovascular disease at subclinical stages, enable prediction of disease progression and prognosis, and facilitate the characterization of disease phenotypes to allow more targeted therapies. Summary: The paradigm is rapidly shifting in the field of echocardiography with the emergence of ML algorithms that are promising to improve data reliability, accuracy, reproducibility, and workflow. Current and emerging evidence suggests that these systems will undoubtedly revolutionize the diagnostic utility of echocardiography both at subclinical and clinical stages and are expected to improve personalized cardiovascular risk assessment. However, widespread implementation of this novel technology will need to overcome challenging regulatory body approval processes. At present, the technology shows promise in improving diagnostic pathways, but evidence of clinical utility is lacking. Large trials will be required to provide robust evidence of ML’s prognostic value in echocardiographic assessment before its implementation in routine clinical practice.