Proteins are essential to various cellular functions by interacting with various ligands, including peptides, small molecules, ions, and nucleic acids. Accurate prediction of protein binding sites is essential for understanding these interactions and their biological significance. Recent advancements in deep learning (DL) have greatly enhanced the accuracy of protein binding site prediction. This chapter provides a comprehensive review of state-of-the-art DL methods for predicting protein binding sites, offering a detailed guide for developing and implementing DL models. It compiles and evaluates methods from recent literature, presenting key details such as model types, input/output data, databases, and evaluation metrics. Additionally, the chapter discusses the data collection and preprocessing steps required for training DL models, highlighting the utility of databases like UniProt, Dockground, PDBbind, and so on. The application of prominent DL architectures, including Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs), is explored, along with recent advancements in these approaches. By providing a thorough overview of these methods and resources, this chapter equips researchers with the tools to advance AI-driven protein binding site prediction.