Thyroid cancer is the most common endocrine malignancy, with papillary thyroid cancer (PTC) accounting for ∼80% of all cases. DNA methylation alterations and gene expression changes in cancer, offer valuable insights into tumor biology and serve as potential clinical biomarkers. However, the functional implications of DNA methylation changes in PTC patients, particularly based on multiomics analysis in the Chinese population, remain insufficiently explored. This study aims to investigate the epigenetic signatures of thyroid cancer and identify the DNA methylation biomarkers for diagnosing PTC in Chinese patients. Thyroid cancer tissues ( = 40) and benign thyroid nodule tissues ( = 31) were collected from Chinese patients for global DNA methylation analysis. Gene expression profiles and H3K27ac modifications were also investigated to understand the impacts of epigenetic changes on gene expression. Genome-wide methylation profiling and machine learning methods were employed to differentiate PTC from control samples. Genome-wide DNA methylation maps revealed significant methylome changes in Chinese PTC tissues. By integrating our data with The Cancer Genome Atlas thyroid cancer methylation profiles, we identified unique hypomethylation patterns associated with thyroid hormone synthesis specifically in Chinese PTC patients. RNA sequencing and H3K27ac modification analysis, along with functional assays, showed that the dysregulated genes in PTC patients are critical for the proliferation, migration, and invasion of thyroid cancer. Our study provides a comprehensive view of the multi-omics-based, function-guided DNA methylation landscape for Chinese PTC patients. We identified seven functional differentially methylated regions with high sensitivity and specificity for diagnosing thyroid cancer in Chinese patients. Additionally, is highlighted as a key player in PTC pathogenesis and shows promise as a valuable biomarker for predicting patient outcomes. This research advances our understanding of DNA methylation in thyroid cancer and underscores the importance of developing population-specific diagnostic tools to improve patient outcomes. However, further validation in larger, independent cohorts is needed to confirm their diagnostic value.