.AI-assisted drug repurposing has emerged as a rapidly growing interdisciplinary field at the intersection of computational pharmacology, biomedical informatics, and translational medicine. Its research structure, dominant algorithms, and clinical productivity remain insufficiently quantified. This bibliometric review analyzes the AI-assisted drug repurposing literature from 2017 to 2025. It maps publication growth, algorithmic clusters, collaboration networks, citation structures, and the movement from computational prediction toward clinical validation. A systematic retrieval of publications from PubMed, Scopus, and Web of Science was performed for the period 2017–2025. VOSviewer, CiteSpace, and custom bibliometric scripts were used to analyze publication growth, co-authorship, keyword co-occurrence, citation bursts, and thematic evolution. The field showed rapid expansion, with publication output increasing from a small methodological niche in 2017 to a broad, multi-cluster domain by 2025. Five dominant algorithmic clusters were identified: network-based inference, transcriptomic signature matching, knowledge-graph reasoning, deep learning, and hybrid multimodal approaches. Computational innovation in AI-assisted drug repurposing is strong, but translation into prospective clinical testing remains limited. Standardized benchmarks, stronger clinical partnerships, and transparent reporting of validation outcomes are needed to improve real-world impact.