Oral Squamous Cell Carcinoma (OSCC) represents up to 90% of all malignancies arising in the oral cavity, with highest incidence in central Asian countries. Owing to the patient-centric molecular heterogeneity in OSCC, there is a pressing need of a personalized approach towards its treatment. In this work, we report a comprehensive biomolecular network of an oral squamous cell to investigate the regulatory dynamics of OSCC using Theatre for in silico Systems Oncology (TISON). The resultant network model consists of key regulatory genes and pathways involved in OSCC. The network is then annotated with normal genomic expression data taken from The Cancer Genome Atlas (TCGA) to validate the output node propensities in the light of physiological inputs using deterministic analysis pipeline. Furthermore, the model was evaluated for its robustness and sensitivity in the light of expression-based physiological inputs. To model histopathological progression of OSCC, stage-specific mutations reported in the literature, were introduced in the network and resultant phenotypic landscapes were validated in the light of published literature. We then constructed personalized patient models, in the light of patient’s expression-based inputs, somatic mutations and copy number alterations, in order to perform personalized therapy. Lastly, personalized in silico therapy was performed through incorporation of potential drugs against each mutation and their phenotypic landscapes were visualized for the reversal of cancer cell to either normal cell fates or apoptosis. The novel drug combinations against mutations, identified using exhaustive screening feature, were used for personalized therapy and resulted in anti-cancer fates or apoptosis in patients. In conclusion, the proposed study provides a systematic roadmap for identification of novel therapeutic targets and precision medicine for OSCC treatment.