Background: Papillary thyroid cancer (PTC) accounts for over 80-85% of all thyroid malignancies and presents a rising global incidence. Necroptosis plays a pivotal role in oncogenesis and immune regulation. However, the prognostic relevance of necroptosis-related genes (NRGs) in PTC remains inadequately explored. This study aims to construct a prognostic model for PTC based on NRGs and evaluate its predictive value for the prognosis of PTC patients. Methods: Using data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), prognostic-related genes (PRGs) were screened via univariate Cox analysis and subsequently refined using least absolute shrinkage and selection operator (LASSO) regularization. Gene set enrichment analysis (GSEA) was then performed for each identified prognostic gene. GSEA was conducted for each PRG, and immune characteristics were analyzed for patients stratified by risk scores. Potential therapeutic agents for PTC were also predicted. Real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to evaluate messenger RNA (mRNA) and protein expression of PRGs in PTC samples. Results: Three PRGs (CXCL5, FNDC4, and TYRO3) were identified. Kaplan-Meier analysis demonstrated a significantly lower survival probability in the high-risk group compared to the low-risk group. Univariate and multivariate Cox analyses confirmed that the risk score was an independent prognostic factor, with a nomogram based on this score offering accurate prognosis prediction for patients with PTC. Notably, immune profiling revealed distinct differences between the high-and low-risk groups. Additionally, qRT-PCR results showed that the expression of CXCL5, FNDC4, and TYRO3 was higher in PTC tissues than in adjacent normal tissues. Conclusions: A necroptosis-related prognostic signature composed of CXCL5, FNDC4, and TYRO3 has been established for PTC. This signature is closely associated with the tumor microenvironment and holds promise for improving both the outcome prediction and long-term monitoring of PTC.