In the digital era, Artificial Intelligence (AI) has transformed auditing processes and financial fraud detection. This research review examines the role of AI in auditor education and the enhancement of fraud detection, aiming to improve the accuracy and efficiency of auditing processes through an analysis of emerging technological applications. The key findings of the study focus on three main themes: the evolution of the auditor’s role (from data collector to strategic analyst), enhancement of fraud detection accuracy, and institutional adoption challenges. Results indicate that Machine Learning (ML) algorithms, particularly Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), outperform traditional models in predicting the likelihood of corporate insolvency. Despite this technical potential, the most significant barriers to adoption are cultural and organizational resistance and deep skill gaps in the current workforce, necessitating an urgent revision of professional competency frameworks. The study strongly underscores that success in this transformation requires immediate operational actions: educational institutions must update curricula with a focus on data-driven auditing, and regulatory bodies must establish algorithm validation frameworks. Furthermore, senior management should implement organizational change strategies and establish digital transformation steering committees to ensure that human professional judgment remains the ultimate accountable authority in auditing processes.