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Simple Summary: Glioblastoma is the most common and aggressive brain tumor in adults. Despite surgery, radiation, and chemotherapy, survival rates remain low, which emphasizes the urgent need for improved therapies. Current preclinical models use untreated tumors, which do not reflect the clinical scenario where patients already receive initial treatments. This review examines the effects of current treatments on the properties of recurrent tumors and evaluates preclinical models that incorporate these standard treatments to better mimic real patient conditions. Improving these models could help to identify more effective treatments, potentially leading to better outcomes for glioblastoma patients. Glioblastoma (GBM) presents a significant public health challenge as the deadliest and most common malignant brain tumor in adults. Despite standard-of-care treatment, which includes surgery, radiation, and chemotherapy, mortality rates are high, underscoring the critical need for advancing GBM therapy. Over the past two decades, numerous clinical trials have been performed, yet only a small fraction demonstrated a benefit, raising concerns about the predictability of current preclinical models. Traditionally, preclinical studies utilize treatment-naïve tumors, failing to model the clinical scenario where patients undergo standard-of-care treatment prior to recurrence. Recurrent GBM generally exhibits distinct molecular alterations influenced by treatment selection pressures. In this review, we discuss the impact of treatment—surgery, radiation, and chemotherapy—on GBM. We also provide a summary of treatments used in preclinical models, advocating for their integration to enhance the translation of novel strategies to improve therapeutic outcomes in GBM. [ABSTRACT FROM AUTHOR]