Recurrent glioblastoma (rGBM) remains one of the most formidable challenges in neuro-oncology due to its aggressive evolution, treatment resistance, and profound intratumoral heterogeneity. Despite advances in multimodal first-line therapy, recurrence is nearly universal and represents a genetically divergent, therapy-adapted malignancy. This review dissects the evolutionary dynamics of rGBM, including clonal selection, therapy-induced mutagenesis, and proneural-to-mesenchymal shifts. It explores the translational potential of longitudinal sampling, circulating tumor DNA, and multi-omics profiling to dynamically monitor tumor progression and resistance emergence. Personalized therapeutic strategies are critically evaluated, including targeted inhibition of EGFR, PI3K/AKT/mTOR, and PDGFR pathways, immunotherapeutic approaches such as CAR T-cell therapy and neoantigen vaccines, and functional drug screening using patient-derived organoids. Moreover, the manuscript highlights innovations in AI-assisted therapy mapping, precision-guided re-irradiation, and adaptive trial designs that redefine individualized care in rGBM. Persistent challenges such as blood-brain barrier penetration, immune evasion, and lack of real-world clinical integration are also addressed. The convergence of high-throughput molecular diagnostics, AI analytics, and targeted therapies underscores a shift from static to dynamic, biomarker-guided interventions. Realizing the full promise of personalized medicine in rGBM demands systemic reforms, multi-disciplinary integration, and equitable clinical adoption.