In 2019 National Cancer Institute (NCI) in the USA ranked lung and bronchus cancer as the second diagnosis of cancer types. It is important to mention that only a few studies have focused on lung and bronchus cancer patient’s survival time by using the SEER database via Machine Learning techniques. This Thesis intends to develop a Machine Learning Approach to classify survivability (dead or survived), and in addition to classification, aims to predict the remaining lifespan for the patients who predicted would die within five years. In the first step, nine Machine Learning techniques, including Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Naïve Bayes Classifier, Ensemble Max Voting, Stacking Ensemble, Random Forest, Gradient Boosting Machine, Adaboost, along with a proposed Deep Neural Network are applied to predict whether the patients would die or survive after five years. In the next step, we use another Deep Neural Network for regression for the patients who are predicted to die for actual survival prediction. The results show that the proposed Deep Neural Network outperformed other Machine learning techniques.