Integrating Artificial Intelligence for Climate- Resilient Energy Planning, Rural Infrastructure Development, and Water Conservation in Underdeveloped Regions

Abstract

Underdeveloped regions are increasingly exposed to climate variability that disrupts energy supply, degrades rural infrastructure, and intensifies water scarcity. These challenges are interconnected and are often exacerbated by limited data availability, weak institutional capacity, and fragmented sectoral planning. Traditional planning approaches struggle to account for climate uncertainty and cross-sector dependencies, resulting in infrastructure investments that are inefficient, fragile, or poorly targeted. The aim of this study is to examine how artificial intelligence can be integrated to support climate-resilient planning across three critical domains: energy systems, rural infrastructure development, and water conservation. The scope of the study focuses on underdeveloped and resource-constrained regions where decision-making must balance reliability, equity, cost efficiency, and long- term resilience under uncertain climate conditions.