This paper presents Gau-Occ, a multi-modal framework for 3D semantic occupancy prediction in autonomous driving that models scenes as compact collections of semantic 3D Gaussians, bypassing traditional dense volumetric processing. The proposed LiDAR Completion Diffuser recovers missing structures from sparse LiDAR point clouds to initialize robust Gaussian anchors, while Gaussian Anchor Fusion efficiently integrates multi-view image semantics through geometry-aligned 2D sampling and cross-modal alignment. By refining these compact Gaussian descriptors, the method achieves both spatial consistency and semantic discriminability for comprehensive 3D scene understanding in autonomous vehicles.