Objective: This study aims to delve into the survival rate and prognostic risk factors of colorectal cancer patients with bone metastases. By constructing and validating an innovative prognostic nomogram, it was possible to more precisely predict the overall survival rate of such patients. This not only improves the prediction accuracy but also offers robust support for clinical diagnosis and treatment decision - making. Methods: The Surveillance, Epidemiology, and End Results (SEER) database of the US National Cancer Institute was utilized to extract the relevant data of colorectal cancer patients from 2010 to 2021. Univariate and multivariate Cox regression analyses were employed to dissect various factors influencing the prognosis of colorectal cancer patients with bone metastases, and a nomogram prediction model was constructed based on these factors. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to assess the effectiveness of the nomogram. Results: A total of 2086 metastatic colorectal cancer patients were screened.Through rigorous analysis, a series of independent prognostic factors were identified, including gender, marital status, age, diagnosis year, primary tumor site, tumor grade, chemotherapy, surgery, N stage, brain metastasis, liver metastasis, and lung metastasis. Subsequently, a nomogram prediction model was successfully constructed. The ROC curve, calibration curve, and DCA curve verification indicated that the nomogram model exhibited excellent predictive performance in evaluating the prognosis of colorectal cancer patients with bone metastases in both the training set and the validation set, and could accurately reflect the trend of the patients’ conditions. Conclusions: By leveraging SEER database resources, this study developed a precise nomogram to predict survival in colorectal cancer patients with bone metastases at 3, 6, and 12 months. The nomogram serves as a robust clinical tool for survival prediction and personalized treatment planning, potentially improving outcomes for this patient population.