Early detection of this disease can readily help to increase the mortality and therapeutic outcomes in patients with lung cancer. However, the disease is complex and mostly detected when it is already in the advance stages which causes high mortality rates. Radiotherapy is commonly used to treat lung cancer, which is a highly prevalent kind of cancer. However, dose-limiting toxicity, including RP, remains a problem. It should be noted that conventional analytical methods for RP prediction with a focus on individual indicators are unsuitable in terms of the interdependent nature of the cancer and treatment characteristics. It is for determining that which of the machine learning algorithms has been efficient in predicting the lung cancer probability most accurately. The following classifiers, namely Naive Bayes, decision trees, bag of trees, logistic regression, and artificial neural network-multilayer perceptron, were used to carry out the prediction on lung cancer. Concerning the accuracy, the results show good potential by providing accuracy of 97% for each algorithm; however, the most accurate one proved to be the multilayer perceptron algorithm with 98.4% accuracy. In summary, this analysis emphasizes the significance of the use of machine learning for early diagnosis and forecasting of lung cancer. Using accurate computational techniques, like the multilayer perceptron algorithm, the clinicians can identify the disease at an early stage thus improving the patient’s outcome and may even reduce fatalities.