This review explores the synergy between forensic accounting and data analytics in identifying fraudulent financial transactions. The convergence of these disciplines has redefined traditional fraud detection methods, enabling higher precision through artificial intelligence, machine learning, and big data analysis. By synthesizing insights from recent empirical studies and case analyses, this paper outlines current methodologies, identifies challenges, and recommends future research directions. These findings highlight the necessity for adaptive and technology-integrated forensic frameworks in a data-driven financial landscape.