The early detection of lung cancer is vital for enhancing patient prognosis, as early-stage diagnosis leads to more effective treatment options and improved survival rates. Logistic regression, a supervised machine learning algorithm, has demonstrated significant potential in identifying early-stage lung cancer by processing clinical, demographic, and radiological data. This method assigns probabilities to various patient outcomes, making it possible to predict the likelihood of lung cancer based on specific risk factors. Logistic regression is favored for its simplicity and interpretability, providing clear insights into which factors are most influential in cancer prediction. Accuracy monitoring plays a crucial role in the deployment of logistic regression for lung cancer detection. Through continuous assessment of model performance using key metrics like accuracy, sensitivity, specificity, and area under the curve (AUC), the system ensures reliability in real-world clinical environments. This iterative process allows for the adjustment and refinement of the model as new data becomes available, improving its predictive capacity. The combination of logistic regression with accuracy monitoring enables healthcare providers to make informed, timely decisions in lung cancer detection, potentially reducing mortality rates.