The Interplanetary AI Nervous System (IANS) is proposed as a scalable and autonomous framework for communication and decision-making in interplanetary missions. This distributed architecture draws inspiration from biological nervous systems, integrating artificial intelligence, distributed computing, and satellite communication to facilitate self-organization, adaptive routing, and autonomous fault recovery. The methodology employs deep learning algorithms and reinforcement learning to optimize inter-satellite data flow and decision-making under the constraints of space environments. Experimental simulations demonstrate that IANS significantly outperforms traditional centralized models, achieving approximately 35% reduction in decision latency and 20% lower energy consumption. The architecture consists of three layers: sensory, cognitive, and execution, enabling efficient data processing and coordination among agents. Additionally, the paper discusses the potential enhancements offered by quantum computing and neuromorphic hardware. Key findings indicate that IANS not only improves reliability and adaptability but also provides a robust framework for future autonomous interplanetary missions, establishing a foundation for AI-driven communication infrastructures in space exploration.