Among those most fatal conditions that necessitate early detection nowadays is lung cancer. Because it can lessen the likelihood of human mistake while evaluating medical images, artificial intelligence has become an indispensable tool in the medical industry and, more specifically, in the analysis of medical images and the diagnosis of diseases. The rapid advancement of machine learning (ML) algorithms for prediction has revolutionized various industries, including medical treatment, facilitating the effortless early detection of lung cancer. Machine learning algorithms have the ability to forecast or diagnose a wide range of serious ailments, including cancer, lung cancer, heart disease, etc., many of which can be dangerous. In this paper, we examine and contrast numerous Machine learning methods that use Boosting algorithms for predicting the onset of diabetes in its early stages. The major objective of this research work is to establish the most efficient classifier for lung cancer detection by organizing and carrying out the procedure using many Boosting based machine learning (ML) techniques. In this study, we examine a broad variety of disease-related traits in an effort to provide a more complete picture of lung cancer and its prognosis. In this research, we employ numerous Boosting algorithm-based Machine Learning classifications strategies to the traditional Lung Cancer Dataset. These techniques include Gradient Boost (GB), XGBOOST (XGB), ADABOOST, CATBOOST (GB), and LightGBM (LGBM). When it comes to accuracy, the models employed here are all over the map. This study demonstrates a method that may reliably forecast the occurrence of lung cancer. This study's findings suggest that the GB Model, a machine learning classifier belonging to the class of Boosting algorithm-based models, is the most effective in predicting the occurrence of carcinoma of the lung.