The project attempts to evaluate the discriminative power of several predictors in the study to increase the efficiency of lung cancer detection through their symptoms. The prominent cause of cancer-related mortality throughout the globe is “Lung Cancer”. Hence beforehand detection, prediction and diagnosis of lung cancer has become essential as it expedites and simplifies the consequent clinical board. To erect the progress and medication of cancerous conditions machine learning techniques have been utilized because of its accurate outcomes. Various types of machine learning algorithms(ML) like Naive Bayes, Support Vector Machine (SVM), Logistic regression have been applied in the healthcare sector for analysis and prognosis of lung cancer. In this review, factors that cause lung cancer and application of ML algorithms are discussed up to date and also draws special attention to their relative strengths and weaknesses. This paper will help the researchers to quickly go through the related literature instead of referring to the many papers.