Autonomous planetary landing is one of the toughest challenges in space exploration, especially in unfamiliar and rough terrains where quick, smart decisions are crucial. This research proposes an integrated system that uses Artificial Intelligence AI, terrain sensing, and vision analysis to improve landing navigation accuracy and safely performance. It combines LIDAR-based elevation maps and CCD images, then it is been analyzed by Convolutional Neural Networks (CNNs) to identify hazards and choose safe landing spots. A landing guidance system using an Extended Kalman Filter (EKF) and PID control adjust the landing path in real time and saves fuel. As a result, simulations show a significant improvement in landing error, it dropped from 150m in previous studies to nearly 20m. In addition, fuel efficiency rose from 70% to 85%, and hazard avoidance improved from 60% to 92%. These results show the ability of the proposed system to make smart decisions on its own, reduce the need for human control, and handle complex surfaces. The frameworkâs flexible design, real-time adaptability and trajectory stability make it a strong candidate for future missions to Mars, asteroids, and beyond.
