The identification of lung cancer is widely regarded as the most challenging issue in the field of medicine. To address this problem, various risk factors have been collected through a series of clinical trials. Recently, the use of machine learning algorithms such as Neural Network, Support Vector Machines, eXtreme Gradient Boosting, Random Forest, and Linear Discriminant Analysis has been explored to predict lung disease outcomes. In this study, we evaluated the performance of these algorithms using metrics such as Accuracy, Precision, Recall, F1 Score, and ROC. Our experimental analysis was conducted on the UCI lung cancer dataset. The results indicate that the XGB algorithm outperforms the other algorithms in the context of lung cancer data.