Planetary robotics is a rapidly evolving field of research that aims to improve the safety and effectiveness of planetary missions. Machine learning (ML) has become an integral part of modern robotics, enabling machines to learn from experience, recognize patterns, and make autonomous decisions. This paper explores how planetary robots use machine learning to enable real-time decision making in extreme, remote environments. The paper will cover the unique challenges of space exploration, the machine learning techniques employed in robotic decision making, and real-world examples from missions like NASA’s Mars rovers and the European Space Agency’s ExoMars program. In addition, the paper will discuss the limitations and ongoing challenges in applying ML to space robotics. The discussion included case studies of current missions that demonstrated the practical applications and achievements of machine learning in planetary exploration. As machine learning advances, it is projected to become even more pivotal in future space missions.