Artificial Intelligence (AI) has emerged as a powerful tool in detecting and preventing fraudulent activities across various industries, including finance, healthcare, and e-commerce. Traditional fraud detection methods often struggle with evolving fraud patterns and large datasets, whereas AI-driven approaches leverage machine learning, deep learning, and anomaly detection techniques to enhance accuracy and efficiency. AI models analyze vast amounts of transactional data in real-time, identifying suspicious activities and reducing false positives. Furthermore, AI-powered fraud prevention systems continuously adapt to new threats through predictive analytics and behavioral analysis. This chapter explores the role of AI in fraud detection, discussing key methodologies, case studies, challenges, and future trends. It also highlights ethical considerations and regulatory compliance in AI-based fraud prevention systems.