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Faced with rising development costs, protracted timelines, and high attrition rates associated with traditional de novo drug development, repurposing leverages known pharmacokinetics, safety profiles, and manufacturing processes to expedite clinical translation. This review synthesizes current advances in computational and experimental methodologies and mechanistic insights that drive drug repurposing for cancer therapy. In silico strategies, including molecular docking, machine learning, transcriptomic\u2010proteomic signature reversal, and network\u2010based modeling, have enabled rapid identification of repurposable agents by mining multi\u2010omics and historical pharmacological data. Experimental pipelines spanning high\u2010throughput screening, phenotypic assays, biochemical validations, and functional animal models remain essential to establish efficacy and delineate mechanisms of action. Notably, repurposed drugs exhibit anticancer activity by modulating key pathways, including phosphatidylinositol 3\u2010kinase\/Ak strain transforming\/mechanistic target of rapamycin, mitogen\u2010activated protein kinase\/extracellular signal\u2010regulated kinase, wingless\u2010related integration site\/\u03b2\u2010catenin signaling, as well as redox homeostasis and DNA response. Despite their promise, repurposed candidates face barriers including limited intellectual property protections, dose optimization challenges, and regulatory uncertainty. Moreover, clinical translation is often hindered by insufficient mechanistic understanding and a lack of predictive biomarkers. Integration of multi\u2010omics datasets, explainable artificial intelligence, patient\u2010derived organoids, and clustered regularly interspaced short palindromic repeats\u2010based genetic screens now offers unprecedented precision in identifying context\u2010specific drug effects and synthetic lethal interactions. With cancer causing one in six global deaths and marked by therapeutic resistance and molecular heterogeneity, drug repurposing provides a scalable solution. 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