<p>Pediatric cardiovascular imaging plays an important role in the diagnosis, monitoring, and management of congenital and acquired heart diseases. Although echocardiography remains the most widely used modality in pediatric cardiology, cross-sectional imaging techniques such as cardiac magnetic resonance imaging (MRI) and computed tomography (CT) provide complementary anatomic and functional information. However, time-consuming diagnostic processes and patient-specific characteristics remain major limitations to early and precise diagnosis, as well as optimal clinical outcomes. With technological advancements, artificial intelligence (AI) has been increasingly integrated into cardiovascular magnetic resonance imaging (MRI) and computed tomography (CT) to enhance image acquisition, segmentation, interpretation, and diagnosis, and to facilitate predictive modeling of clinical outcomes. This review summarizes current and emerging AI applications in pediatric cardiovascular MRI and CT, emphasizing workflow optimization, diagnostic automation, and quantitative analysis. Emerging frontiers include multimodal data integration for risk stratification, clinical decision-making support, digital twin models, three-dimensional virtual modeling, and the application of computational fluid dynamics, as well as the potential of AI to improve access to care in low-resource settings.</p> Graphical Abstract <p></p>