Artificial Intelligence Based Fiber Route Optimization for Cost Efficient FTTH Broadband Expansion Across Underserved American Communities

The persistent broadband access disparity across underserved American communities continues to hinder digital inclusion, economic participation, telehealth access, remote education, and smart infrastructure development despite substantial federal broadband investment initiatives. Fiber-to-the-Home (FTTH) remains the most technically robust long-term broadband solution due to its high bandwidth capacity, low latency, and scalability; however, its widespread deployment is constrained by high capital expenditure associated with route planning, civil works, right-of-way complexities, terrain variability, and inefficient infrastructure utilization. This study proposes an Artificial Intelligence Based Fiber Route Optimization framework for cost efficient FTTH broadband expansion across underserved American communities. The research develops an intelligent optimization architecture integrating Geographic Information Systems (GIS), demographic broadband demand datasets, infrastructure geospatial constraints, road network topology, right-of-way accessibility indices, and construction cost parameters into a unified decision-support environment. Advanced Artificial Intelligence techniques including reinforcement learning, graph neural networks, and multi-objective evolutionary optimization are employed to determine optimal fiber routing pathways that minimize deployment cost, maximize service coverage, reduce trenching distance, and improve network resilience under budgetary constraints. The framework incorporates predictive demand modeling to prioritize deployment zones with the highest socioeconomic impact while accounting for terrain complexity and regulatory bottlenecks. Performance evaluation is proposed against conventional shortest path and heuristic network planning methods using metrics including route efficiency, capital expenditure reduction, coverage expansion ratio, latency optimization, and infrastructure utilization efficiency. The anticipated contribution of this research lies in establishing an intelligent broadband infrastructure planning methodology capable of accelerating equitable FTTH deployment in underserved American regions while enhancing economic sustainability, investment efficiency, and national digital connectivity resilience.