Esophageal atresia is a congenital malformation that requires postoperative care. In recent years, artificial intelligence (AI) has emerged as a promising tool that could improve postoperative follow-up, even in esophageal atresia. The objective of this study is to analyze the application of AI in the postoperative follow-up of esophageal atresia. Bibliometric tools were employed to evaluate the scientific production related to AI in this context. A total of 405 articles published in Medline-indexed journals were analyzed. Topic mapping was used as the primary metric to identify publication trends, offering a analysis of the structure and evolution of AI research, particularly within the field of pediatric surgery. AI is considered to hold potential for improving postoperative follow-up in patients with esophageal atresia and may contribute to advacements in clinic care, although the number of studies to date remains limited. The bibliometrics approach enabled the identification of key and potentially productive areas for future research, thereby supporting strategic decision-making in the planning of scientific projects.