Exposure to carcinogens, particularly those found in cigarette smoke, destroys lung cells DNA and promotes their unchecked proliferation, which is the main cause of lung cancer. Its growth is also greatly influenced by genetic predisposition, family history, and environmental factors such as radon and asbestos. Lung cancer can affect nonsmokers due to these genetic and environmental factors, even though smoking is the main cause. These malignancies have the potential to impair breathing and the exchange of oxygen and carbon dioxide by interfering with the lungs natural function. When applied to tabular datasets, machine learning and deep learning algorithms can help forecast the prognosis of lung cancer and the response to treatment, enabling tailored care plans and enhancing patient outcomes. This study suggests using a convolutional neural network (CNN) architecture to accurately identify lung cancer from medical photos. Clinicians might possibly improve patient outcomes and survival rates by using CNNs’ sophisticated feature extraction capabilities to produce more accurate diagnoses and customized treatment programs.