Radiomics and deep learning computer vision algorithms can extract clinically relevant information from medical images, providing valuable insights for accurate diagnosis of cancerous lesions, tumor differentiation and molecular subtyping, prediction of treatment response, and prognostication of long-term outcomes. In head and neck squamous cell carcinoma (HNSCC), growing evidence supports the potential role of radiomics and deep learning models in predicting treatment response, long-term outcomes, and treatment complications following radiation therapy. This is especially important given the pivotal role of radiotherapy in early-stage and locally advanced HNSCC, as well as in post-operative and concomitant chemoradiotherapy. In this article, we summarize recent studies highlighting the role of radiomics in predicting early post-radiotherapy response, locoregional recurrence, survival outcomes, and treatment-related complications. Radiomics-guided tools have the potential to personalize HNSCC radiation treatment by identifying low-risk patients who may benefit from de-intensified therapy and high-risk individuals who require more aggressive treatment strategies.