: This study aims to develop a prognostic model based on senescence-related long non-coding RNAs (lncRNAs) to predict the prognosis of patients with colon cancer and enhance their survival rates. : Differential expression analysis and Pearson correlation were employed to identify senescence-related lncRNAs in colon cancer. A risk prognosis model was constructed using univariate Cox regression analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. The reliability of this model was validated through survival analysis, receiver operating characteristic (ROC) curves, bar charts, and calibration curves. Additionally, the relationship between the prognostic model, immune microenvironment, and drug sensitivity was explored. : A risk prognosis model comprising eight senescence-related lncRNAs (LINC02257, AL138921.1, ATP2B1-AS1, AC005332.7, AC007728.3, AC018755.4, AL390719.3, and THCAT158) was successfully established, demonstrating strong performance in predicting the overall survival rates of colon cancer patients (AUC = 0.733). A significant correlation was observed between the senescence-related lncRNA prognostic model and the tumor microenvironment, immune cell infiltration, and drug sensitivity ( < 0.05). : The senescence-related lncRNA prognostic model developed in this work can accurately forecast the prognosis of colon cancer patients, offering new insights for personalized treatment approaches in colon cancer.