Detecting financial fraud is a critical concern for banks and businesses worldwide. Traditional rule-based systems and manual investigations have limitations in effectively identifying and preventing fraud in the ever-evolving landscape of financial crimes. Machine learning techniques have emerged as powerful tools for detecting financial fraud by leveraging patterns, anomalies, and behavioral data. These algorithms can analyze vast amounts of data and automatically learn patterns and relationships within it. By training on historical data labeled as fraudulent or legitimate, these algorithms can develop models that detect and classify new instances of potential fraud. Machine learning-based fraud detection offers several advantages. It can adapt and evolve with changing fraud patterns, continuously learning from new data to update detection capabilities. Supervised learning utilizes labeled data to classify transactions, while unsupervised learning identifies anomalies without labeled examples of fraud. Ensemble methods combine multiple models to improve accuracy. However, challenges like data quality, feature engineering, class imbalance, and model interpretability need to be addressed. This study aims to develop a robust and efficient system for detecting financial fraud using machine learning techniques. It involves a comprehensive review of literature, analyzing existing research on machine learning in financial fraud detection. Performance metrics of machine learning models will be evaluated, and hypotheses regarding detection accuracy and time will be tested. Ethical considerations will be maintained throughout the study. The findings will contribute to enhancing fraud detection accuracy, minimizing false positives and negatives, and enabling real-time detection and prevention of fraudulent activities.