This study provides a comprehensive literature review of virtual team (VT) research over the past four decades, utilizing artificial intelligence techniques such as natural language processing (NLP) and topic modeling. Based on a dataset of 2,184 articles from Scopus-indexed journals, the study identifies 16 distinct topics, including communication, leadership, trust, and learning. The analysis reveals an increasing diversification of research topics over time, with key areas like communication, leadership, trust, and learning consistently appearing among the top ten most studied topics. Emerging areas such as agile development and patient care have become prominent in recent years. The study employs BERTopic, a state-of-the-art topic modeling technique, to provide a dynamic and comprehensive overview of the evolving landscape in VT research. The research questions addressed include whether VT research topics have diversified, what the various topics are, and what trends have emerged. The findings indicate that VT research has become more diverse, with topics showing a gradual expansion over time. The study also identifies central topics in VT research, such as student learning, communication, leadership, global and cultural diversity, performance, trust, product design, patient care, global software development, collaboration, knowledge sharing, and agile development. These topics reflect the evolving nature of VT research, highlighting the importance of addressing challenges related to collaboration, communication, and trust in virtual environments. The study underscores the value of using advanced text mining techniques to systematically analyze and understand the trends and developments in VT research.This study provides a comprehensive literature review of virtual team (VT) research over the past four decades, utilizing artificial intelligence techniques such as natural language processing (NLP) and topic modeling. Based on a dataset of 2,184 articles from Scopus-indexed journals, the study identifies 16 distinct topics, including communication, leadership, trust, and learning. The analysis reveals an increasing diversification of research topics over time, with key areas like communication, leadership, trust, and learning consistently appearing among the top ten most studied topics. Emerging areas such as agile development and patient care have become prominent in recent years. The study employs BERTopic, a state-of-the-art topic modeling technique, to provide a dynamic and comprehensive overview of the evolving landscape in VT research. The research questions addressed include whether VT research topics have diversified, what the various topics are, and what trends have emerged. The findings indicate that VT research has become more diverse, with topics showing a gradual expansion over time. The study also identifies central topics in VT research, such as student learning, communication, leadership, global and cultural diversity, performance, trust, product design, patient care, global software development, collaboration, knowledge sharing, and agile development. These topics reflect the evolving nature of VT research, highlighting the importance of addressing challenges related to collaboration, communication, and trust in virtual environments. The study underscores the value of using advanced text mining techniques to systematically analyze and understand the trends and developments in VT research.