Energy consumption estimation plays a crucial role in sustainable development and resource allocation. In this study, energy consumption in the municipalities of Mexico is estimated from night-time light from satellite images. The application of various Statistical and Machine Learning models to estimate energy consumption, such as Linear Regression, Decision Tree, Random Forest, and Neural Network, is explored. This study is the first step toward the creation of a model that allows the prediction of energy consumption in isolated areas where energy consumption data is scarce or there is no information, but night-time light data. The results demonstrate a strong relationship between energy consumption and these satellite images. Consequently, all tested models provided accurate estimations, with the Random Forest and Neural Network methods yielding the best performance.