Objective: To establish nomogram models for predicting the overall survival (OS) and cancer -specific survival (CSS) of gastric cancer liver metastasis (GCLM) patients. Methods: Data from the Surveillance, Epidemiology, and End Results (SEER) database for 5,451 GCLM patients diagnosed between 2010 and 2015 were analyzed. The cohort was divided into a training set (3,815 cases) and an internal validation set (1,636 cases). External validation included 193 patients from the Fourth Hospital of Hebei Medical University and 171 patients from the People's Hospital of Shijiazhuang City, spanning 2016-2018. Multivariable Cox regression analysis identified eight independent prognostic factors for OS and CSS in GCLM patients, including age, histological type, grade, tumor size, surgery, chemotherapy, bone metastasis, and lung metastasis. Two nomogram models were developed based on these factors and evaluated using time -dependent receiver operating characteristic curve analysis, calibration curves, and decision curve analysis. Results: Internal validation showed that the nomogram models outperformed the American Joint Committee on Cancer (AJCC) tumor -node -metastasis (TNM) staging system in predicting 1 -year, 2 -year, and 3 -year OS and CSS in GCLM patients (1 -year OS: 0.801 vs. 0.593, P < 0.001; 1 -year CSS: 0.807 vs. 0.598, P < 0.001; 2 -year OS: 0.803 vs. 0.630, P < 0.001; 2 -year CSS: 0.802 vs. 0.633, P < 0.001; 3 -year OS: 0.824 vs. 0.691, P < 0.001; 3 -year CSS: 0.839 vs. 0.692, P < 0.001). Conclusion: This study developed and validated nomogram models using SEER database data to predict OS and CSS in GCLM patients. These models offer improved prognostic accuracy over traditional staging systems, aiding in clinical decision -making.