This study explores the effectiveness of Machine Learning (ML) and Artificial Intelligence (AI) in addressing financial fraud. It examines the limitations of traditional methods and highlights the potential of ML and AI in improving fraud detection systems. The paper reviews the history of financial fraud, discusses ML and AI techniques, and evaluates their performance using metrics such as accuracy, precision, recall, F1 score, and ROC-AUC. It introduces various algorithms, including Random Forest, Support Vector Machines (SVM), and neural networks, and compares their strengths and weaknesses. The study also discusses the real-world implications of these technologies and outlines future research directions. The paper emphasizes the importance of fraud detection in maintaining financial integrity and highlights the role of AI and ML in enhancing security and efficiency. It concludes that while AI and ML offer significant advantages, challenges such as data imbalance, model interpretability, and real-world adaptability remain. The study suggests future research should focus on improving model transparency, external validation, and the integration of emerging technologies like blockchain and federated learning. The research underscores the transformative potential of AI and ML in financial fraud detection while acknowledging the need for ethical considerations and regulatory compliance.This study explores the effectiveness of Machine Learning (ML) and Artificial Intelligence (AI) in addressing financial fraud. It examines the limitations of traditional methods and highlights the potential of ML and AI in improving fraud detection systems. The paper reviews the history of financial fraud, discusses ML and AI techniques, and evaluates their performance using metrics such as accuracy, precision, recall, F1 score, and ROC-AUC. It introduces various algorithms, including Random Forest, Support Vector Machines (SVM), and neural networks, and compares their strengths and weaknesses. The study also discusses the real-world implications of these technologies and outlines future research directions. The paper emphasizes the importance of fraud detection in maintaining financial integrity and highlights the role of AI and ML in enhancing security and efficiency. It concludes that while AI and ML offer significant advantages, challenges such as data imbalance, model interpretability, and real-world adaptability remain. The study suggests future research should focus on improving model transparency, external validation, and the integration of emerging technologies like blockchain and federated learning. The research underscores the transformative potential of AI and ML in financial fraud detection while acknowledging the need for ethical considerations and regulatory compliance.