Drug repurposing, also termed drug repositioning, refers to the systematic identification of new therapeutic indications for clinically approved or investigational pharmacological agents. This strategy circumvents the lengthy and resource-intensive phases of conventional de novo drug discovery, offering a substantially reduced timeline and lower attrition risk. Artificial intelligence (AI) has emerged as a transformative enabler of drug repurposing, leveraging the exponential growth of biomedical data to extract actionable drug–disease relationships at unprecedented scale. Machine learning (ML) algorithms, including supervised classifiers and ensemble methods, have been widely applied to predict drug–target interactions and adverse effect profiles. Deep learning architectures—such as convolutional neural networks, recurrent networks, and transformer-based models—enable the integration of molecular, genomic, and clinical information in high-dimensional feature spaces. Network-based approaches model the complexity of biological systems by representing proteins, diseases, and drugs as interacting nodes within multi-layered graphs, facilitating the identification of functionally relevant drug repositioning candidates. The integration of multi-omics data, electronic health records, and biomedical literature through AI frameworks has further expanded the scope of computational repurposing. Notable applications include the AI-assisted identification of baricitinib for COVID-19, the repositioning of metformin as a potential anti-cancer and neuroprotective agent, and the application of connectivity mapping to match drug-induced transcriptomic signatures with disease expression profiles. Despite considerable progress, challenges persist in model interpretability, data heterogeneity, and the translational gap between computational predictions and experimental validation. Future advances in explainable AI, federated learning, and multi-modal data integration are anticipated to substantially enhance the clinical translation of computationally repurposed therapeutics.