Radio maps characterize the spatial distribution of wireless conditions and are fundamental to network planning and optimization. However, existing learning-based methods reconstruct isolated radio-map snapshots and do not model how network actions drive the evolution of the radio environment. In this paper, we present RadioDreamer, a two-stage world model that unifies action-conditioned radio-map forecasting and imagination-based network optimization within a latent space. To learn causal environmental transitions from sequential interactions, we introduce a structured spatio-temporal transformer backbone coupled with dynamic-region supervision, enabling accurate modeling of action-sensitive areas. Second, rather than relying on conventional physical trial-and-error, RadioDreamer establishes a general planning paradigm that generates counterfactual latent trajectories to evaluate and optimize long-horizon network parameter configurations. Experiments show that RadioDreamer improves both multi-step rollout forecasting and closed-loop network optimization. It reduces full-map forecasting error by 25.2% on average and achieves approximately 80% accuracy in predicting the evolution direction of radio signals. More importantly, for long-horizon network configuration, imagination-based planning surpasses model-free policies optimized directly on radio-map observations by 8.8% in accumulated return.

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