This systematic review examines how traditional Technology Acceptance Models (TAMs) can be adapted to better understand user acceptance of AI technologies. The study analyzed 80 peer-reviewed articles published between 2010 and 2025, identified through a comprehensive search of EBSCOhost, Embase, Inspec, Scopus, and Web of Science databases using keywords related to AI acceptance factors and technology acceptance models. Our analysis employed descriptive statistics, thematic analysis, and narrative synthesis to identify key factors influencing AI acceptance, including perceived usefulness, perceived ease of use, trust, transparency, explainability, and ethical considerations. We found that trust in AI systems' reliability, fairness, and privacy protection plays a crucial role in user acceptance, with low trust leading to resistance. Transparency and explainability of AI decision-making processes were identified as critical for building user trust. The review also highlights limitations of current research, including inconsistent definitions of AI technologies, inadequate exploration of cultural and contextual differences in AI acceptance, and the predominance of cross-sectional research designs. By addressing these areas, future research can provide a more comprehensive theoretical foundation for understanding user acceptance of AI technologies, guiding the development and ethical application of AI systems.............. Keywords:.......... Artificial Intelligence (AI), Trust and Explainability in AI, Technology Acceptance Models (TAM).