This article presents a comprehensive literature review of virtual team (VT) research, utilizing advanced artificial intelligence techniques such as natural language processing (NLP) and topic modeling. The study aims to analyze the trends and diversification in VT research over the past four decades. By employing the BERTopic model, a state-of-the-art topic modeling technique, the research identifies 16 distinct topics within the VT research domain. The analysis reveals that while key areas such as learning, communication, trust, and leadership have consistently maintained their importance, emerging themes like agile development and patient care have gained prominence. The study also employs the Gini-Simpson Index to measure the diversity of topics over time, showing a significant increase in topic diversity since the late 1990s. The findings provide a dynamic and comprehensive panorama of the evolving landscape of VT research, highlighting both established and emerging areas of focus.This article presents a comprehensive literature review of virtual team (VT) research, utilizing advanced artificial intelligence techniques such as natural language processing (NLP) and topic modeling. The study aims to analyze the trends and diversification in VT research over the past four decades. By employing the BERTopic model, a state-of-the-art topic modeling technique, the research identifies 16 distinct topics within the VT research domain. The analysis reveals that while key areas such as learning, communication, trust, and leadership have consistently maintained their importance, emerging themes like agile development and patient care have gained prominence. The study also employs the Gini-Simpson Index to measure the diversity of topics over time, showing a significant increase in topic diversity since the late 1990s. The findings provide a dynamic and comprehensive panorama of the evolving landscape of VT research, highlighting both established and emerging areas of focus.