In response to new challenges, the application characteristics of dynamic network algorithms were reviewed. These challenges were posed by complex and diversified urban emergency scenarios. Heterogeneous data fusion and algorithmic collaboration were involved. Additionally, approaches for multi-algorithm collaboration and integration were explored. Firstly, based on 128 publications from the past five years in the Scopus database, four major research themes: path planning, traffic regulation, risk prevention, and resilience analysis, which were identified using keyword frequency statistics and cluster analysis. Then, through content analysis, four core algorithm categories: planning, simulation, clustering, and deep learning were summarized. Their theoretical frameworks and task adaptation logic were examined in conjunction with empirical data, simulations, and hybrid data sources. Finally, a multidimensional evaluation system was constructed to comparatively assess the applicability, strengths, and limitations of different algorithms across various emergency scenarios. The results show that multiple algorithms are integrated in application, which include planning, simulation, clustering, and deep learning. This integrated approach enables a multidimensional response to urban emergency management tasks. Such tasks involve path planning, traffic regulation, risk prevention, and resilience analysis. The adaptability of emergency systems are enhanced. The robustness of these systems is also improved. Data-driven intelligent algorithms further improve the responsiveness of urban road networks, supporting real-time strategy adjustment and resource optimization. Future development should focus on the construction of intelligent collaborative architectures for algorithm integration and coupled analysis across multiple networks, to advance the efficient application of dynamic network algorithms in multi-scenario emergency management.