Survival analysis is crucial for patient management and treatment decisions, particularly for those with advanced lung cancer. Lung cancer remains one of the leading causes of cancer-related mortality globally, with survival rates significantly impacted by factors such as age, gender, performance status, and treatment regimens. This study applies the Weibull survival regression model to examine the survival outcomes of patients with advanced lung cancer. A retrospective dataset from the North Central Cancer Treatment Group (NCCTG) comprising 229 patients with advanced lung cancer was used for analysis. The variables under study included survival times, censoring indicators, and a range of covariates, such as age, gender, ECOG performance score, Karnofsky performance score, calorie intake, and weight loss. The Weibull survival regression model was employed to analyze the impact of these covariates on survival time. Model residuals were studied to assess the fit and appropriateness of the model. The survival regression model revealed a significant difference in survival probability based on key covariates. Factors such as ECOG performance score, Karnofsky performance score, and age were found to significantly influence patients' survival. The model provided a good fit for the data, with the residual analysis indicating no major discrepancies. The survival curve showed the impact of covariates and its consistency in the trend. The findings highlight key prognostic factors that influence patients' survival by providing valuable insight for clinical decision-making and personalized treatment strategies. The Weibull survival regression model offers a robust framework for incorporating multiple covariates and predicting patient survival outcomes more accurately.