Glioblastoma (GBM) is the most common type of primary brain tumor. It is very aggressive with minimally effective treatment options. Current therapies yield a median survival of 15 months. Treatment failure may be caused by the highly immune-suppressed glioma microenvironment, leading to a rise in immunotherapy development for GBM. In this talk, a GBM-immune dynamics ODE model is developed, analyzed, and used to identify treatment targets. The model is also extended to include treatment with anti-PD-1 and a CCR2 inhibitor in order to optimize immunotherapy regimens and predict biomarkers for treatment success. Analytical and numerical results suggest that: (1) A treatment plan that includes surgical resection and therapeutics targeting immune suppression caused by the PD-L1-PD1 complex and myeloid-derived suppressor cells (MDSCs) is effective; (2) estimating five parameters, namely the tumor growth rate, the T cell kill rate, the inhibition rates by PD-L1-PD-1 and by MDSCs, and the MDSC death rate, using patient tumor growth data is sufficient to personalize treatment plans; (3) tumor growth rate, T cell kill rate, and MDSC death rate are biomarkers for treatment failure or success; and (4) an optimized treatment regimen improves the survival of an average mouse from 32 to 111~days post-implantation. These results assist in improving treatment targets, regimens, and recommendations for patients suffering from GBM.