This study uses Artificial Neural Networks (ANN) to present a novel approach for the early detection as well as prognosis of lung cancer. Since lung cancer is a major cause of disease and death globally, there is an urgent need for efficient detection techniques. Using advances in machine learning, specifically ANN techniques, the present research outlines novel techniques such as picture categorization for effective lung cancer detection and mitigation. The work focuses on the nuances of disease detection and categorization by using a CNN-based approach to differentiate among malignant, benign, as well as no-normal occurrences. The proposed methodology employs a huge collection of healthcare imaging data, focusing on lung scans, to train the ANN. The network’s advanced design learns many patterns and traits suggestive of lung cancer, enabling accurate and precise predictions. Employing established criteria like sensitivity, specificity, and accuracy, the model’s ability to differentiate among lung pictures that are cancerous as well as those that are not is evaluated.