This study examines the impact of deep learning and big data analytics in the audit industry, particularly within the context of Industry 4.0. Focusing on the integration of artificial intelligence (AI) and machine learning in audit processes. The research analyses 151 peerreviewed publications from Scopus through bibliometric and network analysis. The methodology includes data extraction and analysis using Microsoft Excel and VOS viewer software. Key findings highlight the enhanced efficiency and accuracy brought by technological advancements in complex decision-making and fraud detection, alongside raising critical ethical concerns, notably in bias and privacy within automated systems. The study identifies three main thematic clusters: The Data System in Auditing Techniques, Revolutionize of Audits, and Automated Ethical Decision-making in Audits. It concludes that while AI and deep learning offer significant benefits in audit processes, they necessitate a careful balance between efficiency and ethical considerations, urging a revaluation of ethical frameworks and auditing standards. This research contributes to the understanding of AIenabled audit practices and outlines future directions for adapting audit practices to technological advancements.