Artificial Intelligence (AI) is transforming entrepreneurship by enabling data‐driven innovation, strategic decision‐making, and operational agility; however, its adoption remains uneven and fragmented due to technological, organizational, and contextual barriers. This study examines the applications, trends, and intellectual structure of AI‐driven entrepreneurship through a systematic literature review and bibliometric analysis, employing the SPAR‐4‐SLR protocol to analyze peer‐reviewed English‐language articles retrieved from the Scopus database published between 2004 and 2025. The performance analysis and science mapping of 245 relevant publications was done using Biblioshiny and VOSviewer. The results demonstrate that the research output has increased exponentially since 2018 as a result of the merging of AI technologies and entrepreneurial innovation. Major AI applications have been found in marketing and customer interaction, product development and innovation, decision‐making and strategy, financing, entrepreneurship education, and operational performance. The factors that support adoption include technological readiness, data‐driven culture, and capability development, whereas the hindering ones are the lack of resources, the problem with data quality, and ethical and governance concerns. Thematic and key‐word co‐occurrence analyses demonstrate the emergent research areas of AI‐enabled entrepreneurship, digital transformation, innovation, and generative AI, which highlights the increased interdisciplinarity of the domain. This study presents organized thematic taxonomy and suggests a future research paradigm, which can be of great use to researchers, entrepreneurs and policymakers who would be running AI‐based entrepreneurial ecosystems.