The early phases of disease modeling and target identification are essential to drug discovery, significantly impacting the overall success of drug development. Traditionally, the process of identifying drug targets has been exceptionally time-intensive, often spanning several years to decades, and typically originates within academic research. However, the advent of artificial intelligence (AI) is revolutionizing this domain by utilizing its capability to process extensive datasets and complex biological systems, thereby becoming a pivotal tool in contemporary drug target identification. This chapter explores the latest advancements in target discovery, with a particular emphasis on notable breakthroughs facilitated by AI in identifying therapeutic targets. Additionally, we emphasize the need to balance innovative methods with dependability in target selection. There is an increasing trend of AI-discovered targets undergoing experimental validation, with several AI-developed drugs progressing to clinical trial stages. We also discuss the current limitations in the field and suggest potential future directions.