Head and Neck Squamous Cellular Carcinoma (HNSCC) constitutes the seventh most common cancer diagnosis worldwide, and the long term survival of the affected patients is highly influenced by possible development of distant metastasis and tumor relapse. Hence, a prognostic model able to predict such occurrences would significantly benefit these patients and could be employed for treatment recommendation in order to optimize the handling of the individual subject. In the depicted context, this Thesis aims at describing the development of a multi-modal Deep Learning model for Time to Event analysis of HNSCC patients; our approach involves the employment of clinical and imaging data for the prediction of distant metastasis, loco-regional failure and overall survival of the single patient.