Background The purpose of this present research was to construct a nomograph model to predict prognosis in gallbladder cancer liver metastasis (GCLM) patients so as to provide a basis for clinical decision-making. Methods We surveyed patients diagnosed with gallbladder cancer liver metastasis (GCLM) in the Surveillance Epidemiology and the End Results database (SEER) between 2010 and 2019. They were randomized 6:4 into a training set and a validation set. In the training set, statistically meaningful prognostic factors were determined using univariate and multivariate Cox regression analyses, and an individualized nomogram prediction model was generated. The prediction model was evaluated by C-index and calibration curves from the training set and the validation set. Results 727 confirmed cases were enrolled in the research, 426 in the training set and 301 in the validation set. Factors including bone-metastasis, surgery, chemotherapy and radiotherapy were independent prognostic factors for cancer specific survival (CSS) rates and were employed in the construction of the nomogram prediction model. The C-index for the training set and validation set were 0.685 and 0.709, respectively. Calibration curves indicated that the cancer specific survival (CSS) rates of the nomograph prediction model were consistent with the actual prognosis at 6-months-CSS, 1-year-CSS and 2-years-CSS in both the training set and validation set. Conclusions We have successfully constructed a nomogram prediction model that can predict cancer specific survival (CSS) rates in patients with gallbladder cancer liver metastasis (GCLM). This prediction model can help patients in counseling and guide clinicians in treatment decisions.