<![CDATA[Artificial intelligence has rapidly transformed accounting practices through advanced digital technologies worldwide. This study aims to systematically review the literature on AI applications in accounting and financial reporting, mapping dominant technologies, documented impacts, and critical research gaps. A Systematic Literature Review following PRISMA 2020 guidelines was conducted using Scopus and Web of Science without year restriction. After a four-stage screening process, 25 peer-reviewed articles published in 2025–2026 were retained for thematic synthesis, guided by Agency Theory, the Resource-Based View, and the Technology Acceptance Model. Large Language Models (LLMs) and generative AI are the most prominent technologies applied across financial reporting, auditing, education, and corporate disclosure. Ensemble methods dominate fraud detection and financial distress prediction, while Graph Neural Networks represent an emerging frontier in relational fraud analysis. AI yields measurable improvements in reporting accuracy, audit efficiency, and sustainability accounting; however, critical challenges persist, including LLM hallucination, explainability gaps, regulatory lag, and limited evidence from developing economies. This review is among the first to synthesize AI applications in accounting within the post-generative AI era (2025–2026), integrating evidence across LLMs, GNNs, and explainable AI, and proposing a theoretically grounded research agenda that extends beyond prior bibliometric and domain-specific reviews.]]>
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