To address the issues of low solving efficiency and susceptibility to local optima in multi-unmanned aerial vehicle (multi-UAV) task allocation algorithms within urban areas, this study constructs a task allocation model aiming to minimize economic costs for material delivery and reduce the urgency of rescue task demands. A multi-strategy clustering ant colony optimization algorithm (KMACO) is proposed for solution. Specifically, the K-means clustering method is utilized to partition the number of rescue tasks assigned to each UAV. In the ant colony optimization algorithm, a pheromone update strategy and a random evolution strategy are introduced to guide population search directions, thereby enhancing solving efficiency and avoiding local optima. Experimental results demonstrate that the proposed algorithm effectively reduces algorithm running time and operational economic costs while satisfying rescue task urgency requirements. Compared with conventional methods, KMACO shows superior performance in convergence speed and solution quality, thus providing an optimized decision-making framework for emergency rescue operations in complex urban environments.