Abstract: Traditional multi-ship cooperative inspection search mainly employs fixed partitioning and fails to consider target evasion, resulting in low detection probability and insufficient alignment with actual combat. This paper proposed a multi-ship cooperative search method based on dynamic Voronoi partitioning and multi-source information joint decision-making. Based on a Bayesian probability framework, the method fused a sonar detection model and a target motion diffusion model to construct and dynamically update the probability distribution map of the target’s location. By adaptively dividing the search area using Voronoi diagrams, the method defined responsibility zones for each ship and realized distributed deployment in the task space, which significantly reduced redundant area coverage and eliminated search blind spots. To address the phased adaptation requirements of the “exploration and exploitation” strategy (focusing on exploration in the early stage and exploitation in the later stage), a multi-source information fusion scoring model was designed. This model incorporated target presence probability, degree of unsearched area, and local information entropy into a comprehensive calculation. Furthermore, a mechanism for adjusting weights according to search progress was constructed to dynamically adjust the search strategy with the task process, thereby guiding ships to determine optimal search target points. In adversarial scenarios with active target evasion, the proposed method was compared with the fixed-partition “zigzag”-type area coverage method and the particle swarm maximum probability heading optimization method. Results from 1 000 Monte Carlo simulations indicate that the proposed method significantly shortens the time to discover the target in multi-ship cooperative search tasks and improves the target detection probability in a statistical sense, demonstrating good realism and scalability in adversarial environments.