In today's rapidly evolving intelligent systems landscape, spanning various smart operations such as traffic and energy grid management to deploying sensors for environmental monitoring, optimizing resource allocation is crucial for efficient decision-making and policy formulation. Despite numerous advances in the field of optimization theory, current methods for optimal resource allocation problems often falter, especially in large, distributed networks where devices must coordinate without the presence of a central authority, resulting in slow solutions and high communication demands. This project addresses these fundamental limitations in combinatorial optimization problems, specifically submodular maximization under matroid constraints—an NP-hard problem that regularly appears in optimal discrete resource allocation. While efficient mathematical approaches have been developed to achieve near-optimal solutions in centralized form for submodular maximization under matroid constraints, these solutions often incur significant computational costs. In distributed settings, in addition to these computational costs, the optimal resource allocation process is further exacerbated by high in-network communication costs and local data disclosure issues. Our objective is to develop practical solutions that reduce in-network communication costs, maintain favorable trade-offs in optimality gaps, and accelerate solutions. The project will deliver practical distributed algorithms with an