<p>Cancer is currently one of the leading causes of death in humans. It is characterized by the uncontrolled growth and spread of abnormal cells that form masses called tumors. Cancer is initiated by gene mutations that result in local proliferation of abnormal cells and their migration to other parts of the human body, a process called metastasis. Modeling and analyzing tumor growth dynamics is crucial for the development of more effective cancer treatments to improve patient outcomes. In this study, a novel computational model is presented to analyze tumor tissue growth, incorporating drug resistance, immune response, tumor angiogenesis, and the convective mass flux of tumor cell movement for a more realistic representation of tumor dynamics. The governing equations are solved numerically using a forward-time central-space finite difference scheme. The predictive capabilities of the proposed model are evaluated by investigating the impact of drug therapy on resistance development, the impact of angiogenesis on tumor growth, and the sensitivity of capillary tips to chemotaxis. Key findings reveal that increasing the strength of the chemotactic response from 0.1 to 0.2 leads to an approximately 20% increase in the density of the capillary tips, particularly in regions with greater hypoxia. Angiogenesis and tumor growth are strongly and positively correlated; increased angiogenesis provides the necessary resources for tumor cells to proliferate and expand. Furthermore, the relationship between drug concentration and drug resistance development is nonlinear. These findings highlight the importance of a multifaceted approach to cancer treatment that considers improving drug delivery and targeting, manipulating angiogenic processes, minimizing drug resistance, and integrating personalized treatment strategies. Therefore, policymakers should adopt the recommendations of this study to facilitate the development of more effective and sustainable cancer therapies.</p>