Glioblastoma (GBM) creates an immunosuppressive microenvironment that renders current therapies largely ineffective. The STING (Stimulator of Interferon Genes) pathway has emerged as a potent target to remodel this environment and stimulate local immune responses. This study presents a Finite Element Method (FEM) framework to investigate the therapeutic impact of the STING agonist ADU-S100 on glioma growth. We implemented a coupled reaction-diffusion system on a realistic brain mesh, incorporating tissue heterogeneity by assigning distinct diffusion coefficients to gray and white matter. The model simulates the diffusion of injected ADU-S100 and subsequent intracellular signaling that triggers the production of Type I interferons (IFN-$\beta$), leading to tumor cell death. Simulation results demonstrate that while the control (PBS) group shows rapid tumor expansion along white matter tracks, the ADU-S100 treatment group exhibits significant tumor suppression driven by STING-mediated immune activation. Our results are in good agreement with experimental data (Sean Lawler group, Legorreta Cancer Center, Brown University). This work bridges the gap between computational modeling and mechanistic immunotherapy, providing a predictive tool for optimizing treatment strategies in GBM.