Congenital heart disease (CHD) presents a complex etiology involving multifaceted genetic and environmental interactions. The global prevalence of CHD approximates 8 per 1,000 live births, with elevated rates observed during prenatal periods, attributed to spontaneous pregnancy loss and elective terminations. Timely and precise diagnosis remains fundamental for optimal clinical outcomes, necessitating collaborative efforts among genetic counselors, obstetric practitioners, and pediatric cardiovascular specialists. While conventional diagnostic approaches such as electrocardiography and echocardiography continue to serve as cornerstone tools, sophisticated imaging techniques including cardiac computed tomography and magnetic resonance imaging are increasingly incorporated into clinical practice. Nevertheless, diagnostic challenges persist due to limited clinical recognition, inadequate healthcare infrastructure, and scarcity of specialized practitioners, potentially compromising diagnostic timeliness. Within this framework, artificial intelligence (AI)-specifically machine learning and deep learning technologies-has emerged as a transformative approach in pediatric cardiovascular medicine. AI systems demonstrate capability in identifying complex patterns within extensive datasets, thereby enhancing diagnostic precision, facilitating risk assessment, and enabling personalized therapeutic interventions. Contemporary AI implementations have demonstrated potential in optimizing cardiac imaging interpretation, supporting clinical decision-making processes, and forecasting patient outcomes. Despite promising developments, AI integration within pediatric CHD management remains constrained. Single-institutional studies and the relative rarity of CHD limit data accessibility, emphasizing the necessity for multi-center collaborative research initiatives. Additionally, AI-based systems can enhance postoperative surveillance, simulate therapeutic approaches, and identify complications through wearable monitoring technologies. Such innovations prove particularly valuable in resource-constrained environments where pediatric cardiovascular expertise remains limited. This comprehensive review examines the current state, existing challenges, and future prospects of AI implementation in pediatric cardiovascular medicine. Leveraging AI’s comprehensive potential may revolutionize care delivery pathways, enhance prognostic outcomes, and optimize health management for children with CHD.