The proposed research aims to develop a network-aware learning framework that integrates spatiotemporal graph neural networks, higher-order network models, and deep reinforcement learning to enhance power system forecasting, adaptation, and optimization. By leveraging both synthetic and real-world power grid data, this approach will enable more accurate short-term predictions, adaptive modeling of evolving grid topologies, and data-driven optimization for long-term infrastructure planning and operational decision-making.