"Lung Cancer" is the clearest cause of malignancy-related death worldwide. Then in advance location, the expectation and determination of lung cancer in the lung became fundamental because it accelerates and consequently works in the clinical team. As a result of its accurate results, AI procedures were used to improve the progress of dangerous situations and medicine. Different types of Machine Learning (ML) such as Artificial Neural Network (ANN), Logistic Regression, Support Vector Machine (SVM) and Naïve Bayes have been used in the clinical field to examine and visualize cancer in the lung. Also, various types of deep learning and maximum relevance–minimum redundancy (mRMR) based lung prediction are audited. In this audit, the factors that cause cellular dysfunction in the lung and the use of ML calculations are exceptional, and extraordinary to observe their overall assets and shortcomings. This paper will enable researchers to quickly navigate the attached writing as opposed to referencing multiple papers. Also, future implications of lung cancer prediction are introduced using deep learning.