Background: Eosinophilic Chronic Rhinosinusitis with Nasal Polyps (eCRSwNP) is a subtype of chronic rhinosinusitis characterized by prominent eosinophilic inflammation. Identifying reliable biomarkers is essential for precise and accurate diagnosis. This study integrates bioinformatics analysis, machine learning approaches, and experimental validation to identify and evaluate potential diagnostic biomarkers for eCRSwNP. Methods: Differentially Expressed Genes (DEGs) were identified through gene expression analysis. Key candidate genes were selected using Weighted Gene Co-Expression Network Analysis (WGCNA), Least Absolute Shrinkage And Selection Operator (LASSO) regression, and Support Vector Machine (SVM) algorithms. Quantitative PCR (qPCR) and Immunohistochemistry (IHC) were conducted to experimentally validate the differential expression of HPGDS between eCRSwNP and non-eCRSwNP patient samples. Correlation analyses with clinical parameters, logistic regression modeling, and Receiver Operating Characteristic (ROC) curve analyses were subsequently performed. Results: HPGDS demonstrates significant upregulation in eCRSwNP tissues and correlates with multiple disease indicators, including serum IgE levels, blood eosinophil counts and percentages, tissue eosinophil counts and percentages, Lund-Mackay score, improved Lund-Kennedy score, and certain pathological parameters. Logistic regression analysis and ROC curve analysis demonstrated that HPGDS has high diagnostic accuracy in distinguishing eCRSwNP from non-eCRSwNP. Conclusion: HPGDS may serve as a valuable biomarker for eCRSwNP, offering a novel reference for precise diagnostic evaluation and clinical decision making.