<p>The rapid evolution of deep learning technologies has substantially influenced the field of urban-scene point cloud semantic segmentation (USPCSS), a cornerstone for various applications ranging from road-level applications such as autonomous driving, traffic management, and railway track inspection to urban-level applications such as disaster management, urban planning, and rail infrastructure monitoring. Despite remarkable progress, the complexity of urban environments presents unique challenges. This study conducts an in-depth review of both state-of-the-art and pioneering deep learning models and datasets specifically tailored for USPCSS. First, we categorize the existing mainstream USPCSS datasets into road-level and urban-level, summarize their metadata and unique characteristics, and discuss their associated challenges in semantic segmentation. Second, we classify the USPCSS deep learning models into four categories—image-based, voxel-based, point-based, and fusion-based—highlighting unique strengths and weaknesses, architectures, and innovations. Third, we introduce the mainstream USPCSS evaluation metrics, and conduct comparative analyses of these models on selected datasets. Findings reveal that the recent point-based methods such as PTv3 significantly dominate the accuracy metrics across road-level benchmarks, indicating the robustness of using transformer-based architectures for the direct processing of complex urban-scene point clouds. In the end, we address the pressing challenges and gaps in current research, offering insights into future directions for advancing the USPCSS field. Through this review study, we aim to provide thorough guidance to researchers, engineers, and decision-makers who are seeking comprehensive understanding and cutting-edge solutions in the current field of USPCSS.</p>