Lung cancer is a leading cause of cancer-related deaths worldwide, accounting for approximately 25% of all cancer-related deaths. It is much more deadly than colon, breast, and prostate cancers combined. Early detection and treatment of cancer is crucial for a patient’s recovery. Radiologists use histopathological images to diagnose potentially infected lung regions, but this process can be time-consuming. Deep learning, a type of machine learning, can hasten this process by simulating how the human brain functions. Convolutional Neural Networks (CNNs) can quickly and accurately classify and identify the various kinds of lung cancer, improving patient treatment and survival chances. While many CNN models have been developed by researchers, they often require significant computational power and time. In this project, we developed an improved CNN model that classifies lung cancer types with high accuracy while requiring less computational power than other models. Using metrics like accuracy, precision, recall, and F1 score, our model obtained an accuracy of 98% during training and 97% during validation.