The integration of artificial intelligence (AI) into drug discovery has been transformational, particularly in identifying, validating, and assessing novel drug targets, biological pathways, and therapeutic indication expansion of existing compounds. This chapter examines the application of AI methodologies, ranging from traditional machine learning to advanced deep learning, in streamlining drug development processes. We begin by exploring AI-driven approaches for target and pathway identification, emphasizing how AI models analyze the large amount of biological data available to predict novel and actionable targets. Next, we discuss AI’s roles in accurately and efficiently validating and assessing the predicted target. We then examine AI’s potential for indication expansion, where models predict new therapeutic applications for existing drugs. Finally, we address key challenges and future prospects, highlighting the importance of data quality, model interpretability, and the necessity of implementing regulatory changes to ensure reliable, transparent, reproducible, and robust applications of AI in drug discovery.