Lung cancer is one of the dangerous and life taking disease in the world. However, early diagnosis and treatment can save life. But it is difficult for doctors to identify the cancer at initial stages from CT scan images. Therefore, computer aided diagnosis can be helpful for doctors to identify the cancerous cells accurately. Many computer aided techniques using image processing approaches have shown impressive results in various fields and deep learning has been researched and implemented. It is possible to construct a sustainable prototype model for the treatment of lung cancer using the current developments in computational intelligence. This research deals with two areas related to lung cancer, the first is to review a group of models that help in detecting and diagnosing lung cancer, mentioning its most prominent strengths and gaps within it, and comparing and evaluating the accuracy of those models, or false positive. Deep learning was used to suggest an approach that helps in diagnosing lung cancer. The research touched on introducing deep learning fields and understanding its most prominent characteristics and benefits, specifically in the health sector and healthcare field. This work contributes to the development of existing frameworks for early lung cancer detection, which will enhance the ability to provide solutions more efficiently. Our research provides critical analysis of previous and current lung cancer detection and prognosis approaches, and we aim to make proposals that achieved an impressive 98.38%, while the validation accuracy reached 98.26%. In the future, this will help improve the accuracy of current models for accurate and early diagnosis of diseases, increased treatment options, and reduced symptoms.