Rural and underserved communities continue to face significant challenges in accessing reliable and affordable broadband connectivity due to low population density, high deployment costs, difficult terrain, and limited infrastructure. This study proposes an AI-optimized rural network deployment framework aimed at enabling cost-effective Internet expansion in such regions. The framework integrates geospatial data analysis, machine learning models, and multi-objective optimization techniques to support data-driven decision-making in network planning. Key input variables include population distribution, terrain characteristics, existing infrastructure, socioeconomic indicators, and network performance data. The proposed approach employs artificial intelligence for demand forecasting, coverage prediction, cost estimation, and technology selection, allowing for the identification of optimal deployment strategies tailored to specific rural conditions. Results from the study indicate that hybrid connectivity models combining fiber, fixed wireless access, satellite systems, and community-based networks provide the most effective balance between cost and coverage. The findings also demonstrate that AI-based optimization can improve deployment efficiency, reduce capital expenditure, and enhance service accessibility in underserved communities. In addition, the study highlights the importance of incorporating social-impact indicators, such as access to education and healthcare, into deployment decisions to promote inclusive digital development. While the framework shows strong potential, its effectiveness depends on data availability and requires validation through real-world implementation. This research contributes a scalable and adaptive solution for rural broadband planning, offering valuable insights for policymakers, network operators, and development agencies seeking to bridge the digital divide.