Autonomous navigation in hazardous environments demands path planning strategies that balance computational efficiency with safety considerations. This study compares the performance of three widely used algorithms—A*, D*, and Rapidly Exploring Random Trees (RRTs)—across varying risk conditions. A grid-based framework was employed to simulate three types of environments: mixed-risk scenarios with randomly distributed obstacles, a fully high-risk environment, and a fully low-risk environment. Performance was assessed using execution time, path length, and collision behavior as evaluation metrics. Results demonstrate that A* consistently achieves the fastest execution across all scenarios, confirming its computational efficiency. However, in fully high-risk and low-risk environments, A* tends to generate longer paths compared to RRT. While RRT frequently identifies shorter and more economical paths, its sampling-based approach results in longer computation times than A* and D*, and in some cases, introduces instability in path safety. D* shows performance similar to A* in terms of path length but with slightly higher computation time. Overall, A* emerges as the most reliable option for time-critical applications, whereas RRT offers path-length advantages at the expense of speed and stability. The findings highlight the trade-offs between graph-based and sampling-based methods and suggest that hybrid or risk-aware planners may provide more robust solutions for real-world rescue, surveillance, and hazardous material handling scenarios.