Lung cancer is one of the most fatal forms of cancer, with thousands of people diagnosed each year. Early detection is crucial for improving the chances of survival, as patients diagnosed in later stages have a significantly lower chance of recovery. To address this critical issue, artificial intelligence (AI) can play a vital role in enhancing early diagnosis. Research introduces anautomatic detection deep learning based model to detect lung cancer by leveraging AI, specifically using a convolutional neural network (CNN) model built on the AlexNet architecture. This study was carried out using a public dataset, which consists of medical images that were labeled as benign or malignant. These scans are analyzed by an AI system to aid in evaluating the patient’s condition. To scaffold an assessment of the patient’s condition, the AI system scans those snapshots. The achieved accuracy of 96.20% for the proposed model was remarkable. Other criteria, such as Sensitivity (94.61%) that indicated how accurately the model could identify positive cases and Specificity (95.619%), also depicted excellent performance. They indicate that the AI system is able to offer reliable assistance for healthcare professionals to make the early accurate diagnosis of lung cancer. With the help of such systems, healthcare professionals can have more data-driven decisions which might result in better patient outcomes and better survival. These findings indicate that this AI system can accurately and reliably assist healthcare staff for the early and accurate diagnosis of lung cancer. Medical practitioners would be able to make better, more informed decisions with such systems in place, which could result in better patient outcomes and higher rates of survival.