This work presents a comprehensive computational framework for modeling asteroid mining megastructures as large-scale autonomous space industrial systems. The study integrates principles from orbital mechanics, N-body gravitational dynamics, swarm robotics, artificial intelligence, economic systems modeling, GPU-accelerated parallel computing, and digital twin architectures into a unified simulation environment. The proposed framework enables scalable representation of interplanetary mining operations involving large populations of interacting entities such as asteroids, spacecraft, and autonomous robotic agents. Efficient gravitational simulation is achieved through hierarchical spatial decomposition using Barnes-Hut octree structures, reducing computational complexity and enabling large-scale N-body simulations. Autonomous coordination is modeled using swarm intelligence and multi-agent reinforcement learning techniques, allowing decentralized decision-making and adaptive task allocation across distributed mining systems. A dedicated artificial intelligence layer supports predictive analytics, mission planning, anomaly detection, and system-level optimization under dynamic environmental conditions. A space-based economic model is incorporated to simulate resource extraction, transportation costs, supply-demand dynamics, and industrial reinvestment feedback loops. This allows analysis of long-term sustainability and growth behavior of extraterrestrial industrial networks. To support computational scalability, the framework leverages GPU-based parallel computing for high-performance execution of physics simulation, swarm behavior modeling, and spatial computation tasks. In addition, a digital twin architecture is introduced to maintain real-time synchronization between simulated and modeled system states, enabling closed-loop monitoring and autonomous system control. The study further examines long-term megastructure evolution, including exponential growth regimes, logistic saturation effects, self-replication conditions, and network-based scaling behavior. Collectively, the proposed framework provides a unified theoretical and computational foundation for future autonomous space resource utilization and large-scale interplanetary industrial systems.