Artificial intelligence (AI) and machine learning (ML) have rapidly expanded the possibilities for drug repurposing and therapeutic discovery, driven by advances in deep neural networks, transformer architectures, and large language models. This chapter reviews recent progress in AI-enabled drug repurposing with a focus on applications to Alzheimer’s disease (AD). We highlight influential publications, emerging computational tools, and representative examples of AI approaches, such as network-based models, graph neural networks, generative design, and LLM-guided hypothesis generation, that have been applied to identify novel AD therapeutics. We also discuss current clinical and translational uses of AI/ML in drug development and outline future directions for how these technologies may reshape the AD drug discovery landscape.