The identification of financial fraud in financial statements remains a significant and ongoing challenge in accounting and finance. Addressing this issue is crucial, particularly given recent research developments, including the application of machine learning techniques that offer innovative approaches to detecting fraudulent activities. This study provides a comprehensive review of existing literature, focusing on advancements in enhancing the accuracy of machine learning models for financial fraud detection. It evaluates the methodologies, approaches, and effectiveness of key research conducted over the past decade in generating reliable findings. The results indicate substantial progress in leveraging machine learning for fraud detection. However, concerns remain regarding the quality of these models, underscoring the need for further research involving larger and more diverse datasets. The findings of this review are expected to aid policymakers and professionals in the accounting industry by laying the groundwork for the integration of modern machine learning techniques into fraud detection methodologies.