<p>Distributed spatial networks must maintain connectivity and efficiency despite limited local information and lack of centralized control. We introduce a self-organizing topology control framework in which each node adjusts its interaction range by minimizing a local structural cost function derived from a global Hamiltonian. This distributed optimization process enables the network to evolve dynamically toward low-cost, well-connected configurations. Unlike traditional topology control methods based on protocol heuristics or centralized coordination, our approach formulates network evolution as a physics-inspired energy minimization process. The resulting dynamics exhibit adaptive behavior, scalability, and robustness to perturbations. This framework offers a generalizable strategy for structural optimization in ad hoc, sensor, IoT, biological, and social networks.</p>