The reliable and efficient navigation for mobile robots across challenging outdoor terrains is critical for autonomous robotics. Traditional methods for planning the path of such robots often emphasize minimizing the travel distance but do not accommodate terrain stability, variability, or energy efficiency. The study proposes an integrated approach between satellite-driven geolocation data and terrain-specific features that enhance the path planning strategies in complex outdoor environments. Our method proposes a controller that uses search-based algorithms to generate energy-efficient and dynamically stable trajectories incorporating surface characteristics and environmental data from satellite imagery. By integrating our method, the proposed framework identifies safer and more reliable routes, achieving a significant 32% improvement in traction characteristics compared to the conventional models of path-finding approaches. Our method’s benefits over traditional approaches include improved safety, extended operational efficiency, and the ability to navigate unpredictable and dynamic environments. This makes it ideal for planetary exploration, disaster response in landslide-prone areas, agricultural automation for precision farming in rough terrains, search and rescue operations in earthquake-affected areas, and autonomous delivery systems navigation into rural and unstructured landscapes. It redefines autonomous navigation through terrain-aware planning and delivers a robust performance approach in unpredictable and dynamic environments.