Background:Nasal airway obstruction (NAO) remains diagnostically challenging due to limitations in traditional tools like rhinomanometry and acoustic rhinometry. Computational Fluid Dynamics (CFD) offers patient-specific, quantitative airflow metrics, but clinical adoption is hindered by methodological heterogeneity. This review synthesizes evidence for CFD’s superiority, proposes the first consensus-driven standardization framework, and outlines actionable pathways for clinical integration. Methods:A PRISMA 2020-compliant systematic review identified 32 studies (2009–2023) using patient-specific CFD to assess nasal airflow. Meta-analytic pooling of correlation coefficients (CFD vs. traditional tests) and qualitative synthesis informed a clinically oriented standardization framework. Results:CFD metrics (mucosal heat flux, airflow partitioning) demonstrated stronger correlations with subjective nasal patency (pooled r = 0.78, 95% CI: 0.69–0.85; I2 = 32%, low heterogeneity) than traditional methods ( r = 0.42, 95% CI: 0.31–0.53; I2 = 67%, moderate heterogeneity). Virtual surgery predictions aligned with postoperative outcomes in 85% of cases. The proposed framework standardizes CFD workflows, validation protocols, and reporting criteria. Conclusions: CFD is poised to redefine sinonasal diagnostics. This framework bridges technical and clinical gaps, offering a roadmap for regulatory approval, cost-effective implementation, and AI-driven automation.