Hybrid cloud orchestration is emerging as a crucial component of modern AI strategy, enabling organizations to coordinate resources across on-premises systems, public clouds, edge devices, and quantum services to enhance innovation and control costs. This orchestration goes beyond simple automation by managing dependencies, scaling workloads, and enforcing policies across heterogeneous platforms, addressing challenges like rising cloud costs, data-residency laws, and vendor lock-in. It offers strategic advantages such as cost optimization, improved performance, compliance, and vendor diversification, making it a top priority for enterprises aiming to balance innovation with operational efficiency. Key tools involved include Infrastructure-as-Code, container orchestrators like Kubernetes, and AI-specific platforms such as Kubeflow, all contributing to a unified control plane that abstracts provider-specific APIs. Hybrid cloud orchestration supports AI/ML workloads by enabling secure, scalable deployments and improving resource utilization through techniques like GPU fractioning. As the digital landscape evolves, AI-driven orchestration predicts demand, manages resources efficiently, and aligns with sustainability goals, setting the stage for future advancements like agentic AI and quantum computing integration.