This study presents a comprehensive approach to optimizing hydrogen supply chain network (HSCN), focusing initially on Texas, with potential scalability to national and global regions. Utilizing mixed-integer nonlinear programming (MINLP), the research decomposes into two distinct modeling stages: broad supply chain modeling and detailed hub-specific analysis. The first stage identifies optimal hydrogen hub locations, considering county-level hydrogen demand, renewable energy availability, and grid capacity. It determines the number and placement of hubs, county participation within these hubs, and the optimal sites for hydrogen production plants. The second stage delves into each selected hub, analyzing energy mixes under variable solar, wind, and grid profiles, sizing specific production and storage facilities, and scheduling to match energy availability. Iterative refinement incorporates detailed insights back into the broader model, updating costs and configurations to converge upon an optimal supply chain design. This design encapsulates macro-level network configurations, including centralization versus decentralization strategies, transportation cost analysis, and carbon footprint assessment, as well as micro-level operational specifics like renewable energy contributions, facility scale, and energy portfolio management. The methodology's robustness allows for strategic insights into hydrogen production facility siting, aligning with local energy resources and supply chain economics. This adaptable, multi-scale approach contributes to informed decision-making in the evolution of sustainable hydrogen-based energy systems, offering a roadmap for policy reforms and strategic supply chain development in diverse energy landscapes.