Drug target discovery and identification are crucial aspects of pharmaceutical research, significantly influencing human health and the pharmaceutical market. However, the process of drug discovery and development is often hindered by high failure rates, primarily due to inadequate target selection. With the advancement of experimental techniques and computational power, artificial intelligence (AI) has become a pivotal tool in analyzing vast and complex biological data. This chapter reviews the role of AI in drug target identification, particularly through structure-based and network-based approaches. It explores data sources such as Protein Data Bank (PDB), SCOPe, CATH, and the groundbreaking AlphaFold for protein structure prediction, emphasizing their contributions to understanding druggability. Furthermore, the chapter discusses the application of machine learning and knowledge graph embedding models in predicting protein-drug interactions, highlighting their efficiency and accuracy in identifying potential drug targets. Despite the significant advancements, the chapter also addresses the limitations of AI in drug target identification and suggests future directions for overcoming these challenges.