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Artificial Intelligence (AI) is increasingly transforming teaching and learning in accounting education. While AI technologies offer opportunities to enhance learning outcomes, academic performance is also influenced by broader educational factors such as teacher competence, institutional support, curriculum design, and student engagement. This study investigates the role of AI and educational factors in shaping academic performance in accounting education through a systematic literature review of studies published between 2020 and 2025. Following the PRISMA 2020 guidelines, a structured search of major academic databases yielded 168 initial records. After screening and eligibility assessment, 28 studies were retained for final analysis. The review synthesizes empirical evidence on AI applications in accounting education and identifies key factors influencing student performance. The study is grounded in Human Capital Theory and Social Cognitive Theory, which provide insights into how technological and educational investments influence learning outcomes. The findings reveal that AI technologies such as intelligent tutoring systems, generative AI tools, predictive analytics, and automated assessment platforms enhance academic performance through personalized learning and data-driven feedback. However, the effectiveness of these technologies depends on teacher digital competence, student motivation, curriculum alignment, and institutional support. The study proposes an integrated conceptual framework linking AI adoption and educational factors to academic performance in accounting education. The findings provide implications for educators, policymakers, and curriculum developers seeking to improve accounting education in the digital era.