In the dynamic and unpredictable realm of space exploration, effective management of space complexes necessitates advanced methodologies capable of adapting to complex scenarios and ensuring precise control. Cognitive modeling has emerged as a promising approach, aiming to formalize representations of human cognitive processes within management systems. This paper delves into the principles, role, and evolution of cognitive modeling in the management of space systems. The foundational principles of cognitive modeling encompass several key aspects: the emulation of human thinking, adaptability and learning capabilities, robust data analysis, context-based decision-making, and seamless integration with existing systems. These principles enable cognitive models to replicate human cognitive functions, thereby facilitating more intuitive and effective human–machine interactions. By adapting to the dynamic conditions of space and continuously learning from new experiences, these models significantly enhance the robustness and flexibility of space management systems. The role of cognitive modeling in optimizing decision-making is underscored by its ability to provide flexible, adaptive, accurate, and intelligent solutions. Cognitive models offer a high degree of flexibility, swiftly adapting to changing environmental conditions and new scenarios. They also enhance the accuracy of decision-making processes through sophisticated algorithms that mimic human cognition. Learnability is another critical feature, allowing systems to evolve based on past experiences, thereby improving their performance over time. The evolution of cognitive modeling algorithms has seen the integration of advanced technologies such as machine learning and neural networks. These advancements have enabled the development of more efficient algorithms capable of real-time adaptation to complex and variable conditions.