Equitable access to essential community services remains a persistent challenge in rural and underserved regions, where dispersed populations, limited infrastructure, and resource constraints complicate planning decisions. This study explores the use of geospatial machine learning as a decision-support framework for optimizing the placement of rural infrastructure and improving access to critical services such as healthcare, education, water, and transportation. By integrating spatial data on population distribution, mobility patterns, service catchment areas, travel times, and socioeconomic vulnerability, the proposed approach identifies locations where infrastructure investments yield the greatest equity gains. Machine learning models, combined with geographic information systems (GIS), are used to detect spatial inequalities, predict service accessibility gaps, and evaluate alternative placement scenarios under real-world constraints. The framework emphasizes interpretability and policy relevance, enabling planners to balance efficiency with social equity objectives. Results demonstrate that geospatial machine learning can significantly enhance infrastructure siting decisions by reducing travel burdens, improving service coverage, and prioritizing high-need communities often overlooked by traditional planning methods. The findings highlight the potential of data-driven, spatially explicit tools to support inclusive rural development, strengthen evidence-based policymaking, and promote more equitable distribution of public resources in low-density and resource-constrained environments.