The thesis delves deeply into innovative methodologies aimed at enriching our comprehension of urban building energy dynamics. By merging the principles of Urban Building Energy Modeling with the strength of Machine Learning (ML) techniques, the study achieves substantial advancements in evaluating potential energy savings and harnessing renewable resources within Satom. The research journey involves developing a sturdy building model, employing Geographic Information System (GIS) software to enhance modeling precision and data aggregation. Furthermore, by incorporating state-of-the-art ML algorithms like LightGBM and Random Forest through a bottom-up strategy, the study offers accurate forecasts of energy patterns and effective renewable energy utilization. Moreover, the fusion of conventional methods like Multiple Linear Regression with ML presents an all-encompassing view of energy dynamics.