<p>Generative Artificial Intelligence (AI) has rapidly emerged as a transformative research domain across multiple disciplines. This study aims to systematically map the evolution, intellectual structure, and research trends of Generative AI through a large-scale bibliometric analysis. A total of 5,960 publications indexed in the Web of Science database between 1981 and May 2024 were analyzed using advanced bibliometric and scientometric techniques. Using Bibliometrix/Biblioshiny (R) for descriptive analysis, trend topic analysis, thematic evolution, and keyword co-occurrence analysis, while VOSviewer was employed for network-based analyses, including co-authorship, co-citation, and keyword co-occurrence visualization. The results indicate a rapid growth in Generative AI research, with an annual growth rate of 17.92% and contributions from 16,901 authors. The United States, China, the United Kingdom, South Korea, and Germany emerge as the most influential countries, while MIT and the Chinese Academy of Sciences lead among institutions. IEEE and Springer Nature are identified as the most prominent publishers. Thematic analysis reveals emerging themes such as ChatGPT and large language models, alongside foundational themes including deep learning and machine learning. Network-based clustering identifies two dominant knowledge clusters: Artificial Intelligence and Deep Learning. Overall, this study provides a comprehensive overview of the Generative AI research landscape and offers insights into its intellectual structure, thematic evolution, and future research directions. The findings offer valuable implications for researchers, policymakers, and practitioners by informing strategic research planning, interdisciplinary collaboration, and evidence-based decision-making in the development and governance of Generative AI.</p>