Lung cancer is currently the main illness causing an increase in human mortality at an alarming rate. It is imperative to foresee malignant lung nodules early on in the course of lung cancer. Deep learning is becoming increasingly important in this modern era for the early diagnosis of medical imaging. The computer-aided diagnosing method benefits from deep learning. This study uses a novel Multilayer Convolutional Neural Network (MCNN) model with six layers—two dense, one flattening, and one convolution and max pooling—to identify preprocessed CT scan lung images according to features taken from a dataset of CT scan lung cancer images. It is used to learn complex features from the images, which leads to better accuracy. On the model’s performance evaluation, 96.25% accuracy is attained. The suggested model operated.