The rapid shift toward multi-cloud architectures has provided organizations with unparalleled scalability and vendor flexibility, yet it has simultaneously introduced a fragmented security landscape that exceeds the capacity of manual oversight. As data assets and workloads proliferate across diverse platforms such as AWS, Azure, and Google Cloud, maintaining a cohesive security posture becomes a critical challenge. This review article explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) into cloud governance frameworks to establish a \"Secure Multi-Cloud Architecture.\" By leveraging AI-driven automation, organizations can achieve real-time anomaly detection, predictive risk modeling, and continuous compliance monitoring across heterogeneous environments. The following sections provide a comprehensive analysis of the architectural requirements, the role of AI in policy orchestration, and the transition from reactive security to proactive, autonomous governance. Ultimately, this review highlights how AI serves as the linchpin for managing the complexity of modern, distributed cloud ecosystems while ensuring robust data protection and regulatory alignment.