Abstract Gliomas are the most prevalent, aggressive, and invasive subtype of primary brain tumors, characterized by fast cell growth, strong invasion capability, and well-developed tumor vasculature. Although the research advances and the clinical trials testing the efficacy of novel combined therapies have allowed significant progress in the comprehension and treatment of gliomas, these tumors are characterized by a poor prognosis, and recurrence remains the main cause of mortality. The growth and migration of tumor cells inside the brain is a highly complex phenomenon, influenced by a multitude of intrinsic and extrinsic factors at different spatial and temporal scales. The high complexity of this invasion process remains a challenge to face and several important questions are still unanswered. In this context, mathematical models emerge as powerful tools to face these challenges. Here, we propose a multiscale approach for the description of glioma progression under the influence of vascularization. Precisely, we firstly focus on the effects of (hypoxia-driven) acidity and phenotypic heterogeneity on tumor progression. Then, we extend the setting to include vascular endothelial growth factor evolution and compare various therapeutic approaches. Starting from a microscopic description of the interactions between specific cell membrane receptors and the extracellular microenvironment, we define two different coupled systems for endothelial and tumor cell evolution. The analysis of these systems allows us to assess tumor growth and spread on the basis of DTI and glioma patient data. In particular, we perform several data-driven numerical tests aimed at assessing the role of vasculature and phenotypic heterogeneity on tumor progression and comparing the effect of different combinations of radio-, chemo-, and anti-angiogenic therapy on it.