Background: Chronic inflammatory rhinosinusitis (CIRS) is a prevalent ENT condition whose burden is amplified in elderly patients by immunosenescence, polypharmacy, and atypical clinical presentations. Conventional management strategies show significant limitations in this population. Objective: To review the current evidence on artificial intelligence (AI) applications for the diagnosis, treatment planning, and follow-up of CIRS, with a focus on elderly-specific challenges and opportunities. Methods: A narrative review of the literature was conducted using PubMed, Cochrane Library, and Google Scholar. Search terms included “artificial intelligence,” “machine learning,” “deep learning,” “chronic rhinosinusitis,” “elderly,” and “decision support.” Articles published between 2013 and 2025 in English were included. Results: AI demonstrates significant potential across all phases of CIRS management: automated CT sinus segmentation, endoscopic polyp detection, biotherapy response prediction, post-FESS recurrence-risk modeling, and intelligent remote monitoring. In elderly patients, AI’s capacity to integrate comorbidities and detect atypical imaging patterns yields clinically meaningful advantages. Conclusion: AI represents a pivotal step toward precision medicine in elderly CIRS management. Widespread clinical integration requires rigorous validation on geriatric cohorts, ethical governance, and structured clinician training.