Immunotherapy (IT) has recently emerged as an important modality for the treatment of advanced-stage cancers. Despite the encouraging clinical results, the numberof patients who durably respond to IT alone is small. Therefore, there has been clinical interest in combining IT with other cancer treatment modalities, particularly radiotherapy (RT). The combination of IT and RT, known as immunoradiotherapy (IRT), has shown great clinical promise to induce both local and systemic anti-tumor immune response, leading to a durable disease control. However, RT may also promote immune suppression by killing highly radiosensitive immune cells. The immune stimulant and suppressive effects of RT depend on the timing and dose of radiation and the immunotherapeutic agent. The primary goal of this research is to develop a mathematical model that captures the interplay between RT and IT by modeling the interactions between tumor cells and immune cells under the influence of radiation and immune checkpoint inhibitors (ICI’s). Mathematical modeling of IRT can play a significant role in hypothesis generation for the design of promising clinical trials with the ultimate goal of improving the treatment efficacy for cancer patients. A compartmental model is developed to simulate the population dynamics of tumor and immune cells. The proposed compartmental model accounts for primary and inactivated tumor cells, metastatic tumor cells and recruited lymphocytes. The model is incorporated into an optimization framework to determine the best IRT treatment schedule. A Monte-Carlo simulation approach is employed to account for parameter uncertainties in the model. Computational results focus on advanced-stage non-small cell lung cancer (NSCLC), which is commonly treated by IRT. In particular, to estimate the model parameters, the progression-free and overall survival curves obtained from the MC approach are fitted to the Kaplan-Meier survival curves reported from IRT clinical trials for NSCLC. The calibrated model is then used to design an IRT treatment regimen by optimizing the time-averaged progression-free survival for a patient cohort.