Cancerous skin lesions (i.e., skin cancers) are the most common types of cancer. Currently, there exists an unmet clinical need to develop advanced imaging tools for skin diagnosis and associated treatment monitoring. In this work, we reported the development of a smartphone-based multi-spectral imaging (MSI) system to fulfill the need and validated its performance through radiodermatitis assessment: one of the severe side effects of skin cancer radiotherapy (RT) treatment that needs to be objectively graded to avoid the possible RT treatment cessation thereof. In combination with a graphics processing unit (GPU)-accelerated Monte Carlo simulations code, Monte Carlo look-up table (MCLUT)-based skin-layer-resolved physiological parameters [i.e., the blood volume fraction (BVF) and the oxygen saturation of hemoglobin in blood] were derived from the multi-spectra measured. The mean square error (MSE) between the MCLUT-derived spectra and the experimentally acquired multi-spectra was found to be less than 0.0031, confirming the robustness of the physiological parameters derived. Further analysis of variance (ANOVA) conducted on the BVF and the oxygen saturation of hemoglobin in blood revealed a significant (P < 0.001, ANOVA) increase in erythema. Additionally, partial least square discriminant analysis (PLS-DA) implemented on the multi-spectra leads to an overall classification accuracy of 85.9% for the separation between normal skin and erythema, confirming the potential of the smartphone-based MSI system developed for radiodermatitis assessment.