The emerging applications of next-generation wireless networks demand high-fidelity environmental intelligence. 3D radio maps bridge physical environments and electromagnetic propagation for spectrum planning and environment-aware sensing. However, most existing methods treat visual and wireless data as independent modalities and fail to leverage shared electromagnetic propagation principles. To bridge this gap, we propose URF-GS, a unified radio-optical radiation field framework based on 3D Gaussian splatting and inverse rendering for 3D radio map construction. By fusing cross-modal observations, our method recovers scene geometry and material properties to predict radio signals under arbitrary transceiver configurations without retraining. Experiments demonstrate up to a 24.7% improvement in spatial spectrum accuracy and a 10x increase in sample efficiency compared with NeRF-based methods. We further showcase URF-GS in Wi-Fi AP deployment and robot path planning tasks. This unified visual-wireless representation supports holistic radiation field modeling for future wireless communication systems.