Effective understanding of maritime scenarios plays a pivotal role in shaping the fate of Navy ships. Each Navy ship has a set of static and dynamic resources that are exclusive to it with diverse ranges of fields of view (FOVs), orientations, and possible locations. Multi-agent maritime scenarios often get very complex, and prior knowledge of vulnerable areas around a ship is crucial for preventing collisions and safeguarding those areas. We propose a dynamic resource allocation algorithm for maritime ships that is adaptive to a ship’s vulnerable areas, as well as, potential threat hotspots at any point in time. We investigated the problem of asset allocation for multiple defenses, sensors, coastal guards with binoculars, and pilot vessels belonging to the USS Arleigh Burke Destroyer ship model (DDG-51). This is a non-deterministic polynomial-time hard (NP-Hard) problem that required searching through a space of \(2^4\) \(^3\) parameters and resulted in an overall fitness value of \(68\%\) representing overall coverage in a given area around a navy ship within 35 generations. In contrast to traditional resource allocations that maximize overall coverage around a ship, our algorithm incorporates the distance to the closest point of approach (dCPA)and time to the closest point of approach (tCPA) of opponent ships. Inclusion of this criterion guarantees maximum possible coverage in the most vulnerable regions of a Navy ship by allocating resources near and orienting them towards potential threat hotspots. Thus, the overall outcome is the prevention of collision and enhanced safety in the most vulnerable regions around a ship.