<![CDATA[Purpose – This study systematically and longitudinally examines the development of AI research in accounting and auditing, identifying its intellectual structure, thematic evolution, and global research dynamics from 1990–2025.Methods – Using a bibliometric approach, 2,419 documents from Scopus were analyzed via Bibliometrix (Biblioshiny) in R and VOSviewer, covering performance analysis, co-occurrence mapping, collaboration analysis, and thematic evolution.Findings – Scholarly output accelerated markedly after 2015, with over two-thirds of publications appearing in the last decade, reflecting AI's growing integration into accounting and auditing practice. Themes evolved from conceptual automation discussions to data-intensive applications in audit quality, financial reporting analytics, governance monitoring, fraud detection, and decision support. Thematic mapping shows increasing convergence among AI, big data analytics, and governance research, signaling a shift toward predictive, real-time accounting functions. While research remains concentrated in developed economies, rising contributions from emerging countries indicate growing global diffusion.Research implications – Limited to Scopus-indexed publications, the study nonetheless offers a comprehensive view of the field's intellectual and thematic trajectory, providing a foundation for future research on underexplored contexts, interdisciplinary integration, and AI's implications for accountability, transparency, and decision usefulness.Originality – This study offers an integrated 35-year bibliometric review spanning both accounting and auditing, extending beyond prior reviews focused on single technologies or isolated domains. By combining performance analysis, science mapping, collaboration analysis, and thematic evolution, it reveals how AI scholarship has progressed from technology-oriented applications toward broader concerns of governance, reporting quality, accountability, and data-driven decision-making.]]>
