Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) are recognized to have significant effects on industry and business dynamics, not least because of its impact on the preconditions for entrepreneurship. There is yet a lack of knowledge of GenAI as a theme in entrepreneurship research. This paper presents a systematic literature review aimed at identifying and analysing the evolving landscape of research on the effects of GenAI on entrepreneurship. We analyse 83 peer-reviewed articles obtained from leading academic databases: Web of Science and Scopus. Using natural language processing and unsupervised machine learning techniques with TF-IDF vectorization, Principal Component Analysis (PCA), and hierarchical clustering, five major thematic clusters are identified: (1) Digital Transformation & Behavioural Models, (2) GenAI-Enhanced Education & Learning Systems, (3) Sustainable Innovation & Strategic AI Impact, (4) Business Models & Market Trends, and (5) Data-Driven Technological Trends in Entrepreneurship. Based on the review, we discuss future research directions, gaps in the current literature as well as ethical concerns raised in the literature. We pinpoint the need for more “macro-level” research on GenAI and LLMs as external enablers for entrepreneurship and research on effective regulatory frameworks that facilitate business experimentation, innovation and further technology development.