Current research focuses on analyzing existing scientific production and exploring emerging trends in the application of AI in the tax field. The ability of machine learning algorithms to process large volumes of data at high speed has opened up new possibilities in the tax space. From accurately detecting tax evasion patterns to personalizing tax services, AI is radically transforming the way tax administrations interact with taxpayers and manage tax systems. Using bibliometric methods and the Scopus database, this study aims to identify new scientific trends regarding the impact of artificial intelligence on taxation processes. The results showed an average annual growth of 7.56%, indicating a positive trend. The data showed a constant growth in scientific production since 2019 (91) until reaching a peak in 2022 (139). Regarding the sources found, Lecture Notes in Networks and Systems is the most relevant with 27 documents, followed by Advances in Intelligent Systems and Computing with 20 documents and Lecture Notes in Computer Science (including subseries) with 19 documents. According to the results found, both Kumar R and LI J share the first place in terms of number of published papers, with 5 publications each; the rest of the most relevant authors have 4 publications each, with the exception of Almada M (3). With 12 associated articles, Aalborg University is positioned as the institution with the greatest presence in the search results, followed by the Rostov State University of Economics, with 11 associated articles. In conclusion, this bibliometric study lays the foundation for future research in this interdisciplinary area. The results obtained can be useful for researchers, professionals and decision makers interested in understanding the implications of artificial intelligence in the tax field.