With the increasing urban rail transit network and the closer connection among operational subsystems, the potential influence range and management difficulty of emergencies have greatly increased. The current emergency management based on traditional methods has some shortcomings in real-time risk recognition, integration of multi-source information and cross-departmental collaborative decision-making. By integrating a comprehensive literature survey with typical case analyses, this paper investigates the use of Internet of Things (IoT), big data, artificial intelligence (AI) and digital twins (DTs) in urban rail transit emergency management. The research is carried out from three main aspects: risk monitoring and early warning, emergency decision-making and collaborative response, and passenger evacuation and operation recovery. The results show that intelligent techniques offer assistance in the monitoring of operational status, anomaly detection, emergency information exchange and evaluation of evacuation plans, which can effectively improve the processing efficiency of information and the supportive ability in decision-making. However, at present, the actual implementations are limited by insufficient data sharing between different systems, restricted model generalization, lack of validation in practical operational situations, and network and data security risks. In the future, more efforts should be made to standardize multi-source data and sharing mechanisms, strengthen the validation of intelligent models and DT systems under real operational conditions, and optimize human-in-the-loop collaborative decision-making to improve the reliability and practicability of intelligent technologies in urban rail transit emergency management.