Background: Hebei Province is a high-risk area for gastric cancer in China, and there is currently no survival prediction model for gastric cancer patients in Hebei Province. This study aimed to build the best survival prediction model for gastric cancer patients in Hebei Province.Methods: The development dataset included 1,993 hospitalized gastric cancer patients from the Hebei Cancer Registration Project during 2016 to 2017. Three tree-based machine learning methods [survival trees (ST), random survival forests (RSF), and gradient boosting machines (GBM)] and Cox, were used to develop the models by ten-fold cross validation with 200 iterations. California Chinese hospitalized gastric cancer patients were used as external test models. In addition, we compared the multivariable group with the Tumor Node Metastasis (TNM) group.Results: The 3-and 5-year cancer-specific survival (CSS) rates of the development dataset were 57.07% and 44.48%, respectively. For predicting the 3-year CSS rates of gastric cancer patients of multivariable group, the C-indexes in train datasets were 0.75, 0.72, 0.79 and 0.76 for Cox, ST, RSF and GBM. Multivariable group performed better than TNM group. The predictive ability of Cox and RSF were superior to ST and GBM. A nomogram was established to predict the 3-and 5-year CSS rates of gastric cancer patients.Conclusions: The nomogram was useful for facilitating clinicians to predict the survival of gastric cancer patients, and identifying high-risk patients so as to adopt more reasonable treatment plans.