<p>Glioblastoma Multiforme (GBM) is a highly aggressive and lethal form of brain tumor, presenting significant challenges in treatment and management. This study proposes an advanced control model for the management of GBM, focusing on the optimization of drug therapies and adaptive regulation of tumor volume. Leveraging fractional-order mathematical modeling, the approach integrates mathematical modeling techniques with control theory to develop comprehensive strategies for GBM management. It introduces a novel fractional-order mathematical model that uniquely integrates nutrient dynamics and blood flow into the analysis of Glioblastoma Multiforme (GBM) progression. By combining fractional-order calculus with adaptive PID control, the approach provides a more accurate representation of tumor behavior and enables personalized, real-time treatment optimization. The proposed control models aim to optimize drug therapies by dynamically adjusting treatment parameters based on real-time tumor volume monitoring. This adaptive approach allows for personalized treatment regimens tailored to the specific characteristics of the tumor, enhancing therapeutic efficacy while minimizing adverse effects. The proposed framework advances current methodologies by addressing memory effects and long-range dependencies in tumor growth, offering new insights for precision medicine. By harnessing the capabilities of fractional-order modeling, our research provides a novel framework for advancing the management of GBM, offering new insights and strategies for combating this formidable disease.</p>