Combining visual semantics with language representations has made 3D interpretation more flexible and intuitive. Recent advances in Gaussian Splatting extend this to efficient 3D language fields supporting open-vocabulary queries. However, existing approaches show limited generalization in large urban scenes, especially for detailed building segmentation. Semantic 3D city models such as CityGML, by contrast, provide hierarchical and geometry-aligned structural semantics that complement appearance-driven visual cues. We introduce CityLangSplat, which integrates CityGML semantics into 3D Language Gaussian Splatting for urban environments. CityLangSplat rasterizes CityGML into pixel-aligned semantic maps, extracts vision-language features from SAM-derived segments and CityGML regions, and compresses both sources into a shared latent space via a lightweight autoencoder. 3D Gaussians are then optimized with a coverage-aware loss that balances accurate, building-focused CityGML supervision with broader SAM supervision, enabling geometry-aligned open-vocabulary reasoning in urban scenes. Experiments on TUM2TWIN and ZAHA datasets show consistent gains over LangSplat, with relative improvements of 22.9% in 2D and 15.1% in 3D evaluation while preserving real-time rendering. CityLangSplat provides a practical framework for combining semantic city models with language-embedded 3D Gaussian Splatting for geometry-aligned urban scene interpretation. Code will be released at https://github.com/zqlin0521/CityLangSplat.