Cancer is a complex and dynamic disease capable of rapidly spreading throughout the body and impairing the immune system. Although immune cells can directly attack tumor cells or activate other components of the immune response, their activity alone is insufficient to achieve complete tumor eradication. Consequently, from a healthcare systems engineering perspective, optimized treatment planning and scheduling are essential to maximize clinical efficacy and resource efficiency. This study employed MATLAB Simulink simulations to model the system dynamics and investigate the effects of chemotherapy and immunotherapy, both individually and in combination, on cancer and immune cells. Cellular population dynamics were first analyzed in the absence of treatment, followed by separate and combined evaluations of each therapy, with particular attention to cellular interactions and cancer cell drug resistance. This approach enables a systematic evaluation of treatment as a multi-objective optimization problem, balancing tumor clearance and immune preservation. The findings revealed that in patients with small initial tumor size and robust immune function, the immune system alone is capable of eliminating cancer cells without therapeutic intervention. However, in patients with large initial tumor size, the combined application of chemotherapy and immunotherapy was predicted to be effective, achieving complete tumor clearance within 38 days while preserving overall health. Collectively, the simulation results suggest that combined chemo-immunotherapy, especially when the sequence of administration is carefully considered, is predicted to be the most effective strategy among the tested protocols and may serve as a model‑based decision support tool for personalized healthcare management, pending prospective clinical validation.