Artificial intelligence (AI) and machine learning (ML) techniques play an increasingly crucial role in the field of drug repurposing. As the number of computational tools grows, it is essential to not only understand and carefully select the method itself, but also consider the input data used for building predictive models. This review aims to take a dive into current computational methods that leverage AI and ML to drive and accelerate compound and drug target selection, in addition to addressing the existing challenges and providing perspectives. While there is no doubt that AI- and ML-based tools are transforming traditional approaches, especially with recent advancements in graph-based methods, they present novel challenges that require the human eye and expert intervention. The growing complexity of OMICs data further emphasizes the importance of data standardization and quality.