Aiming at the problems of weak target search capability, poor coordination, and limitations in the field of view and attack capabilities of single-domain platforms in unknown suburban battlefield environments, this study proposes an air-ground coordinative dynamic target search and strike strategy based on IWPA-HDMPC and call mechanism. Models of air-ground agents and battlefield environments are proposed while constructing environmental map to enhance agents’ perception and search representation. Under the framework of distributed model predictive control (DMPC), objective functions are established, and an improved wolf pack algorithm with hierarchical mechanism is introduced to accelerate solution efficiency and improve search benefits. At the same time, the algorithm satisfies communication and safety constraints. Considering agent heterogeneity, a joint mission model integrating target search and coordinative strike is designed to provide agents with a closed-loop search-decision-action process. In simulation instantiated with real-world regions, compared with the latest method, the proposed method improves static area coverage efficiency by 25.9% and dynamic target discovery speed by 40%, demonstrating enhanced search efficiency and superior mission execution capability.