Semantic segmentation of 3D point clouds is pivotal for urban modeling and autonomous systems, yet challenges like irregular data structure and complex geometry hinder accurate segmentation. This study explores integrating the 3D Medial Axis Transform (MAT)—a topological skeleton encoding shape geometry via maximally inscribed balls—into deep learning frameworks to enhance semantic reasoning. We propose a feature fusion approach embedding MAT-derived attributes (radii, separation angles, medial bisectors) into point-based (PointNet++) and graph-based (Superpoint Graph) networks, enabling explicit geometric context for local points and superpoint relationships. Experiments on diverse datasets (3DOM, SynthCity, SHREC) demonstrate that MAT-enhanced features, particularly radii and separation angles, improve mean intersection over union (mIoU) by 5.8–12.4% compared to baseline RGB-only models, especially for classes like grass and shrubs where appearance features are ambiguous. However, MAT-guided geometric partitioning requires careful regularization to avoid over-segmentation, and graph convolutions benefit most from mean MAT attributes for global structure modeling. This work establishes MAT as a valuable geometric prior for point cloud segmentation, highlighting its potential to bridge topological structure and data-driven learning.