Artificial intelligence (AI) is transforming head and neck cancer (HNC) care by enhancing diagnostic accuracy and personalizing treatment strategies. This review explores AI technologies, including deep learning and natural language processing, and their applications in HNC. AI integrates with imaging, genomics, and electronic health records to improve early detection, biomarker discovery, and treatment planning. Despite progress, challenges like data quality, algorithmic bias, and interdisciplinary collaboration remain. Emerging innovations such as explainable AI, AI-powered robotics, and real-time monitoring systems hold promise for advancing the field. Addressing these challenges is crucial for developing equitable and effective AI applications. The future of AI in HNC offers potential breakthroughs in diagnostics, personalized therapies, and improved patient outcomes. AI is also playing a key role in early detection, biomarker discovery, prognostic and predictive models, and personalized treatment planning. AI enhances the analysis of medical images, identifies patterns in histopathological data, and improves the accuracy of lesion detection. It aids in the discovery of biomarkers by analyzing complex datasets, including radiomic, genomic, proteomic, and metabolomic data. AI-driven risk assessment models predict disease progression and patient outcomes, enabling more accurate and individualized risk predictions. Personalized treatment planning uses AI to tailor treatment regimens based on individual patient data, improving efficacy and minimizing adverse effects. AI also enhances monitoring and surveillance by tracking disease recurrence and patient compliance with treatment regimens. In drug discovery, AI accelerates the identification of new therapeutic targets and drug compounds, offering efficient and precise solutions. Overall, AI is revolutionizing HNC care by improving diagnostics, treatment, and patient outcomes.Artificial intelligence (AI) is transforming head and neck cancer (HNC) care by enhancing diagnostic accuracy and personalizing treatment strategies. This review explores AI technologies, including deep learning and natural language processing, and their applications in HNC. AI integrates with imaging, genomics, and electronic health records to improve early detection, biomarker discovery, and treatment planning. Despite progress, challenges like data quality, algorithmic bias, and interdisciplinary collaboration remain. Emerging innovations such as explainable AI, AI-powered robotics, and real-time monitoring systems hold promise for advancing the field. Addressing these challenges is crucial for developing equitable and effective AI applications. The future of AI in HNC offers potential breakthroughs in diagnostics, personalized therapies, and improved patient outcomes. AI is also playing a key role in early detection, biomarker discovery, prognostic and predictive models, and personalized treatment planning. AI enhances the analysis of medical images, identifies patterns in histopathological data, and improves the accuracy of lesion detection. It aids in the discovery of biomarkers by analyzing complex datasets, including radiomic, genomic, proteomic, and metabolomic data. AI-driven risk assessment models predict disease progression and patient outcomes, enabling more accurate and individualized risk predictions. Personalized treatment planning uses AI to tailor treatment regimens based on individual patient data, improving efficacy and minimizing adverse effects. AI also enhances monitoring and surveillance by tracking disease recurrence and patient compliance with treatment regimens. In drug discovery, AI accelerates the identification of new therapeutic targets and drug compounds, offering efficient and precise solutions. Overall, AI is revolutionizing HNC care by improving diagnostics, treatment, and patient outcomes.