In finance and more specifically accounting and auditing fields, machine learning (ML) alongwith artificial intelligence (AI) have been increasingly used in the last few years. This paperprovides a systematic review of the selected articles depicting the use of ML and AI tools inaccounting research in the last thirty-eight years. The ‘Library’ database of Dalarna Universityand the PRISMA protocol is applied for data search technique of the top 20 journals ofaccounting to get the most relevant and optimised data. The evolution of the application of MLand AI use in the field of accounting research in the last 38 years has been analysed. ‘LDA(Latent Dirichlet Allocation) Topic Modeling’ has been applied to check the dimensionalityand diversification in the collected data. Human classification was then applied to validate theoutcome of topic modeling. The results had shown that there is concentration of research workon specialized use of ML and AI in accounting research in last decade or so. The topicmodeling results obtained during this systematic review were summarized under five-topicdimensionality i.e. ‘ audit decision / prediction’, ‘financial risk analysis’, ‘firms decisionmaking’,’ business fraud analytics’ and ‘identify financial model’ so that shows that these arethe major arenas on the subject of ML and AI in accounting research. This thesis can be of itskind that provides methodology of applying topic modeling for systematic review at least inthe field of accounting. Given the limitations of ‘Library’ database and LDA topic modeling;expanding the use of different databases and application of other topic modeling algorithmslike NMF (non-negative matrix factorization), for future research are recommended.