<![CDATA[The fast-paced digitalization of audit practice has accelerated the adoption of artificial intelligence (AI), but systematic understanding of research patterns, empirical findings, and persistent gaps in this domain is still limited. This research is based on a systematic review by using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, with peer-reviewed articles in 2022 to 2026, obtained from reputable international and national journal databases, based on a literature review. Thirty primary articles were retained after a rigorous process of identification, screening, and assessment of eligibility and represented. The synthesized studies have consistently shown that AI technologies, especially machine learning, deep learning, natural language processing and robotic process automation, can significantly improve the fraud detection accuracy (85-96.3%), the audit process efficiency (40-70%), and the quality of risk assessment compared to the traditional methods. However, common structural barriers across contexts limit AI adoption: limited auditor competence, algorithmic bias, erosion of professional skepticism, and lack of uniform regulatory standards. The review concludes that AI is a complement, not a substitute, for human expertise, and successful implementation depends on the confluence of technology, ethical governance and auditor digital literacy. The findings chart the research path of AI in auditing and suggest a future research agenda, especially regarding cross-industry validation and evidence-based regulatory framework development.]]>
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