Lung cancer is caused when cells divide in the lungs uncontrollably. These can reduce a person’s ability to breathe with both inhale and exhale. Direct Cigarette smoking and passive smoking are the Main contributor for Lung Cancer as per WHO. The mortality rate is increasing every day in youths as well as in old persons due to lung cancer as compared to other cancers. In this study, Machine learning algorithms are used for Predicting lung cancer survival on Surveillance, Epidemiology, and End Results (SEER) and to predict weather patient is affected or not affected on Kaggle dataset. Several pre processing steps were done on data before applying Classification Algorithms. Classification Algorithms used on both the Datasets are Logistic Regression, Naïve Bayes, Random Forest and Support Vector Machine(SVM). Also Neural Network and k-Nearest Neighbor (k-NNN) are two additional algorithms used on SEER Dataset. The efficiency of the algorithm is calculated in terms of accuracy for SEER Dataset and Prediction of Affect is done using accuracy and Specificity for Kaggle Dataset. Logistic Regression and Random Forest showed the best result for Kaggle Dataset and Random Forest and Naïve Bayes for SEER Dataset. These Machine learningbased technique help in predicting the lung cancer using supervised classification machine learning algorithms accurately. And Finally GUI based Interface is used for Kaggle dataset and Orange software is used for SEER dataset.