The rapid adoption of multi-cloud and hybrid cloud environments has created significant challenges in resource optimization, cost management, and performance governance across distributed infrastructures. This paper examines how AI-driven approaches are transforming multi-cloud strategies by enabling intelligent workload distribution, predictive scaling, and adaptive resource allocation across public, private, and edge computing platforms. We explore the application of machine learning techniques for dynamic cost optimization, latency reduction, and compliance maintenance—including standards like GDPR and SOC 2—in heterogeneous cloud environments. Challenges such as interoperability, transparency, and trust in autonomous systems—especially in serverless and quantum-cloud architectures—are analyzed. Combining insights and capabilities of cloud computing and AI/ML, we present a unified view of intelligent cloud orchestration. The paper concludes with practical recommendations for adopting AI-enhanced strategies, guiding enterprises toward scalable, cost-effective, and secure cloud operations.