Advances in computational modeling for oncology are revolutionizing our ability to forecast the outcomes of cancer therapies, potentially enabling their optimization and personalization. By capturing the complex multiscale dynamics of tumor growth, mass transport of therapeutic agents, microenvironment interactions, and treatment response, these models provide critical insights that bridge experimental and clinical studies. Computational models offer a powerful framework for investigating mechanisms of treatment resistance, selecting optimal combination regimens, designing more effective drug delivery systems, and tailoring interventions to individual patients through patient-specific simulations and digital twin technologies. By linking tumor biology with microenvironmental factors (i.e., vascularization, hypoxia, stromal interactions) and integrating principles of drug design and delivery, multiscale modeling supports the development of more precise and effective therapeutic strategies. Despite these advances, significant challenges remain in model fidelity, parameterization, and integration into real-world clinical decision-making. This underscores the need for close collaboration between computational scientists, experimental researchers, and clinicians. This mini-symposium will highlight cutting-edge developments in computational approaches for cancer therapy, including: • Physics-based modeling of tumor biophysics, drug delivery, and treatment effects across scales. • Rational design of new molecules, drug delivery systems, and their synergistic interaction. • Data-enhanced frameworks for therapy optimization and resistance prediction. • Digital twins for clinical decision support and treatment personalization. • Mathematical and computational challenges in model validation, parameter estimation, and clinical translation. We welcome contributions that address these themes through novel modeling approaches, computational methods, or translational applications. The session aims to bring together researchers working at the intersection of mathematical modeling, computational science, and clinical oncology to advance patient-specific cancer care.