The advancement of technology has not only benefited not only the manufacturing, transportation, and automation sectors but also the medical field, which has improved people’s quality of life. Nowadays, artificial intelligence (AI) and machine learning (ML) not only handle large amounts of data but also provide fast and precise results analysis, and they also aid in problem-solving and decision-making. Since lung cancer takes the lives of a huge population, it poses a serious threat to human health and requires an early diagnosis. This study aims to find the correlation between tuberculosis and lung cancer. Also, it compares the accuracy, precision, recall, and F1-scores of multiple ML classifiers trained independently to anticipate the onset of lung cancer. The proposed model is the ensemble ML model in which the final decision is based on the combined predictions of the most accurate classifiers. In the experimental study, an ensemble of different ML classifiers was chosen, and it is observed that the ensemble of multilayer perceptron and logistic regression gives the highest accuracy as compared to the individual classifiers. The superiority of the ensemble machine learning on top of the other standalone ML classifiers is revealed by the analysis of the results on the lung cancer dataset. An accuracy of 96% is achieved by the ensemble of LR and MLP, outperforming all other classifiers. Theoretically, this research indicates that there is a correlation between lung cancer and tuberculosis, also experimental results shows that the ensemble ML approach can achieve increased prediction accuracy.