Many articles have been published in gifted education in recent years. This study aims to provide a comprehensive review of the evolution of academic studies in gifted education. In this context, the structural topic modeling (STM) method was used to analyze the topics and trends in the field. STM is a machine learning technique that utilizes natural language processing techniques based on text mining. It is a valuable methodology for identifying a text corpus's main topics and trends. The corpus used in this study is 5,127 articles from nine leading journals in giftedness without any year limitations. As a result of the analysis, five topics that prominently emerged in the literature were discovered. These are curriculum and instruction, social-emotional characteristics, thinking skills, identification and assessment tools, and equity and policies. The research topics and trends discovered due to the analysis are discussed within the literature framework, and recommendations are presented.