Optimizing train skip-stop operations is a key strategy for enhancing urban rail transit service quality and reducing operational costs. This paper systematically reviews research progress in this field, first outlining major stopping patterns (all-stop, zonal-stop, express/local, stop-skipping) and their research evolution. It then details skip-stop benefits including reduced travel time, balanced passenger flow, and lower energy consumption and emissions, noting the growing emphasis on energy efficiency aligned with dual-carbon goals. Current limitations include computationally intensive models unsuitable for real-time application and inadequate adaptation to dynamic passenger flow. Future research should integrate big data and AI for dynamic adaptive optimization using short-term forecasting, while enhancing system recovery capability and social equity in operational strategies.