Building energy optimization is a crucial focus in sustainable practices, and building information modelling (BIM) data is an invaluable resource for enhancing energy performance. Deep learning algorithms, a subset of machine learning techniques, exhibit great potential in utilizing BIM data to optimize energy use and assess potential energy savings. This paper reviews diverse studies utilizing different algorithms, including support vector machines (SVM) and artificial neural networks (ANN), to optimize building energy use based on BIM data. The investigations encompass HVAC system performance, orientation, geometry data, etc. The findings demonstrate that deep learning algorithms, particularly ANN, offer significant enhancements in building energy efficiency, leading to substantial energy reductions and potential savings. This abstract provides a comprehensive overview of the current state-of-the-art in leveraging deep learning algorithms, with a specific emphasis on ANN’s superior performance in optimizing building energy use based on BIM data.