This article explores the construction and validation of predictive models using the SEER database to forecast survival among various types of thyroid cancer patients. The study encompasses 48,700 cases diagnosed between 2000 and 2018, encompassing different pathological types. Utilizing multivariate logistic regression, it analyzed seven variable factors: age, gender, T, N, M stage, race, and multiple primary malignant neoplasms (MPMNs) information. Assessing their correlation with clinical features and prognosis among thyroid cancer patients. Based on these factors, diagnostic models predicting survival rates among different pathological types of thyroid cancer patients were established. The research indicates that gender, age, and MPMN information significantly impact patients' survival times across various pathological types. The model demonstrates good predictive performance, offering high accuracy in forecasting the survival rates of thyroid cancer patients with diverse pathological types. Additionally, Kaplan-Meier survival curves based on risk stratification exhibit consistently lower survival rates among patients in high-risk groups. The article highlights the initial impact of multiple primary tumors on thyroid cancer prognosis and emphasizes that relying solely on tumor information is insufficient for precise survival predictions. It underscores the necessity of collecting additional treatment information and data to enhance comprehensive research into thyroid cancer prognosis.