Purpose of Review <p>Echocardiography serves as the primary diagnostic and surveillance non-invasive imaging modality for pediatric and congenital heart disease in fetal, pediatric, and adult patients. Echocardiography for this patient population is often constrained by anatomical complexity and rarity of lesions, significant inter-operator variability, and a global shortage of specialized expertise. Artificial intelligence (AI), specifically through machine learning and deep learning, is revolutionizing all steps of the echocardiography “imaging pipeline.”</p> Recent Findings <p>The more recent application of AI into pediatric and fetal echocardiography has yielded a number of promising studies for improving the efficiency, reproducibility, and accuracy of all aspects of the imaging pipeline. These include opportunities to enhance image acquisition and quality, automate view classification, automate measurements, and structure segmentation, improve disease detection, diagnosis, and fetal screening, enhance risk stratification and precision medicine, and create clinical decision support systems.</p> Summary <p>This review explores the integration of AI in image acquisition and optimization, automated view classification, structure segmentation, and diagnosis and clinical decision support systems. By synthesizing foundational principles from the medical literature from 2020 to 2025, we outline how AI is transitioning from a primarily research-based interest to a clinical instrument in the quest for precision pediatric cardiology.</p>