Lung cancer stands as one of the most devastating diseases world-wide, with timely detection remaining a significant challenge. Machine learning emerges as a pivotal ally in this battle, offering avenues to integrate and dissect the vast and intricate datasets that characterize lung cancer from multifaceted perspectives. Among the arsenal of machine learning techniques, ensemble methods shine as potent tools to enhance the prediction accuracy of classifier learning systems. Pioneering heterogeneous ensemble learning approach (ELA) was introduced for the early prediction of lung cancer. This approach amalgamated Bagging, Adaptive boosting (AdaBoost), and three classification algorithms K-Nearest Neighbour (KNN), Random Forest (RF), and Support Vector Machine (SVM) as base classifiers. The evaluation focused on lung cancer survival prediction, demonstrating the efficacy of ensemble methods in assessing the performance of base classifiers and their suitability for cancer survival analysis. The methodology comprised four distinct stages: preprocessing of image data, segmentation, feature extraction, and classification. These stages facilitated the categorization of benign and malignant cases. Crucial parameters such as accuracy, recall, and precision were meticulously calculated to gauge the classifier’s efficacy. The results strongly underscored the superiority of ensemble methods in enabling early detection of lung cancer.