Large‐scale earthquake disasters in recent years have caused huge damage to people's lives and property. Quick effective emergency response is the primary task post‐disaster. A dynamic network flow model can be used to obtain an effective emergency material rescue plan for the transportation network. Strong timeliness is a critical factor to emergency response. However, very few efficient algorithms were presented for most dynamic network flow problems. To solve this issue, this article introduces a novel low‐time‐complexity exact algorithm using the continuous‐time dynamic network flow (CTDNF) and the base state with amendment (BSA) model in the geographic information science field. Besides, this new CTDNF‐BSA algorithm is improved to solve more complex cases with dynamic capacity and transmit time in a dynamic transportation network. Both numerical and geographic experiments were conducted to assess the correctness, time and space performance of the presented CTDNF‐BSA algorithm. The experimental results demonstrated that the CTDNF‐BSA algorithm can attain the optimal solution with good computing time and space performance as compared with traditional algorithms. Geographic cases in dynamic situations for real emergency response illustrated that the CTDNF‐BSA algorithm can provide effective decision support for emergency material rescue planning in a dynamic transportation environment.