This paper presents a comparative study of three nature-inspired optimization algorithms—Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO)—for solving a search-and-rescue pathfinding problem in a simulated grid-based environment. The objective was to find the optimal path from a start point to a goal while avoiding obstacles, with a cost function that penalizes invalid paths such as those that traverse obstacles or exceed grid boundaries. Each algorithm was evaluated based on its ability to minimize the path cost, defined by the sum of travel steps and penalties. The results demonstrated that PSO and GWO achieved a cost of 10, indicating efficient navigation and optimal or near-optimal solutions, whereas ACO yielded a significantly higher cost of 2029. The paper analyzes the performance differences, attributing the superior results of PSO and GWO to their more structured and adaptive search strategies. PSO benefits from a balance of individual and global best solutions, while GWO utilizes a hierarchical search mechanism inspired by wolf pack behavior. This study underscores the effectiveness of PSO and GWO in grid-based pathfinding and suggests future research directions, including adaptation to dynamic environments and diverse grid configurations.