The convergence of artificial intelligence and cloud computing has revolutionized fraud prevention strategies across global enterprises. This review examines the strategic integration of these technologies, analyzing their synergistic impact on detecting, preventing, and mitigating fraudulent activities. We explore how AI-powered algorithms leverage cloud infrastructure to process massive datasets in real-time, enabling predictive analytics and adaptive threat responses. The paper investigates implications for business performance metrics, operational risk management, and organizational resilience. Through comprehensive analysis of implementation frameworks, security considerations, and performance outcomes, we demonstrate that organizations adopting integrated AI-cloud solutions achieve superior fraud detection rates, reduced false positives, and enhanced operational efficiency. Critical challenges including data privacy, algorithmic bias, and integration complexity are also examined. The convergence of artificial intelligence and cloud computing has revolutionized fraud prevention strategies across global enterprises. This review examines the strategic integration of these technologies, analyzing their synergistic impact on detecting, preventing, and mitigating fraudulent activities. We explore how AI-powered algorithms leverage cloud infrastructure to process massive datasets in real-time, enabling predictive analytics and adaptive threat responses. The paper investigates implications for business performance metrics, operational risk management, and organizational resilience. Through comprehensive analysis of implementation frameworks, security considerations, and performance outcomes, we demonstrate that organizations adopting integrated AI-cloud solutions achieve superior fraud detection rates, reduced false positives, and enhanced operational efficiency. Critical challenges including data privacy, algorithmic bias, and integration complexity are also examined.