Strategic leadership plays a pivotal role in facilitating the acceptance and adoption of artificial intelligence (AI) in higher education. By aligning institutional goals with technological innovation, leadership shapes stakeholder perceptions, manages resistance, and ensures ethically guided implementation. However, current literature lacks a comprehensive synthesis on how strategic leadership specifically influences AI integration within academic institutions. To examine the impact of strategic leadership on the acceptance and adoption of AI in higher education, with a focus on stakeholder perceptions, leadership styles, and institutional readiness. A systematic review was conducted following PRISMA 2020 guidelines. Searches were performed in Web of Science and Scopus databases, covering literature published up to early 2025. Inclusion criteria focused on empirical, peer-reviewed studies addressing strategic leadership and AI adoption in higher education. Exclusion criteria removed studies unrelated to leadership, lacking empirical validation, or focused on non-tertiary education. Out of 438 initial records, 10 studies met the eligibility criteria. Data synthesis was narrative, supported by structured coding and thematic analysis using RStudio®. The review included studies from 8 countries, involving faculty, students, and administrators. Leadership styles such as transformational and paradoxical leadership were consistently associated with higher AI acceptance. Common barriers included resistance due to ethical concerns, lack of trust, and technical complexity. Effective leadership mitigated these challenges through clear vision, targeted training, and ethical frameworks. Despite positive outcomes, a moderate risk of bias was observed in most studies due to cross-sectional designs. Strategic leadership significantly enhances AI acceptance in higher education by aligning innovation with institutional values and addressing resistance. The findings offer practical implications for leaders aiming to foster ethical and inclusive AI adoption. Future research should incorporate longitudinal and mixed-method approaches to strengthen causal inferences.