In recent decades, the financial system has been involved in fraudulent or equivalent oppressive practices. Financial fraud is a significant concern with wide-ranging implications for various sectors, including the finance industry, government, corporations, and average consumers. Expanding reliance on emerging technologies like mobile computing and the cloud has recently exacerbated the issue. The concerns emphasized in this commentary demonstrate the intricate nature of fraud and how it can substantially impact the results obtained from financial fraud research. Machine learning fraud detection techniques significantly contribute to preventing financial and operational catastrophes and mitigating losses for businesses. As a result, machine learning is an indispensable component of any viable solution for continuous fraud protection. An optimized machine learning algorithm as well as deep learning algorithms for detecting financial forgeries has been reviewed as part of this research, along with their outcome, research gaps, and highlighting the future scope of machine learning models.