This paper presents Speculative Policy Orchestration (SPO), a novel latency-resilient framework designed for cloud-robotics manipulation tasks. The proposed approach enables robots to offload computationally intensive motion planning to remote cloud servers while maintaining stable high-frequency control at the edge. By utilizing a cloud-hosted world model to pre-compute and stream kinematic waypoints, the system effectively decouples execution frequency from network round-trip latency. To ensure safe operation, an epsilon-tube verifier bounds kinematic execution errors, preventing unsafe predictive drift. Additionally, an Adaptive Horizon Scaling mechanism dynamically adjusts the speculative pre-fetch depth based on real-time tracking performance. The framework is validated through continuous manipulation experiments on RLBench under emulated network delay conditions.