Angiogenesis is known as the key factor in vascular tumor growth. In addition to furnishing the tumor with fresh nutrients to survive and spread to the other tissues, tumor-induced vasculature is tortuous and contains leaky vessels. Therefore, anti-angiogenesis is introduced as a way to suppress tumor growth and preclude metastases. Secondly, method of normalization of tortuous tumor-induced vasculature is being used to ease drug delivery to the tumor site. And thirdly, combined therapies of chemotherapy and radiotherapy with anti-angiogenesis agents can help achieve better outcomes in cancer treatment. One endogenous anti-angiogenesis agent that is being used in the aforementioned treatment strategies is known to be endostatin. In this research, a novel formulation is proposed and a computer code is developed to study anti-angiogenesis effects of endostatin. It is shown that endostatin as an endogenous agent can suppress angiogenesis and normalize tumor-induced vasculature which can ease drug delivery to the tumor site. By increasing endostatin concentration to 5 times of its natural concentration in the blood plasma of cancer patients, angiogenesis could be tackled and hindered. Finally, in order to propose a general and simple formulation to predict final microvessel density at different circumstances, a Generalized Regression Neural Network (GRNN) is established. The results of GRNN show that it is able to predict the microvascular density with 87% accuracy. With the aim of GRNN formulation, scientists can observe the vasculature at any desired conditions to get an insight on optimum time for the combined treatment.