Energy poverty is a pressing issue in agricultural areas that affects the livelihoods of millions of people worldwide. The lack of access to modern energy services in rural communities hinders the development of the agricultural sector and limits economic opportunities. To address this issue, this thesis aims to develop a predictive modeling framework using machine learning techniques to identify feasible interventions that can improve energy access in specific rural agricultural regions. Machine learning plays a pivotal role in addressing energy poverty in rural agricultural regions. By leveraging the power of advanced data analytics and predictive modeling, machine learning algorithms can analyze vast datasets related to energy usage, agricultural practices, geographic factors, and socioeconomic conditions. These algorithms can uncover valuable insights and patterns that are not readily apparent through traditional analytical methods. Moreover, machine learning enables the development of predictive models that can forecast energy demand and identify optimal strategies for improving energy access in rural areas. These models can take into account various variables, such as crop cycles, weather conditions, and community needs, to recommend interventions that are tailored to the specific requirements of each region.