Respiratory illness is the primary factor behind fatality associated with cancer globally, and timely identification is imperative for preserving lives. However, analyzing CT scans can be a burdensome task for radiologists and observer fatigue can lead to reduced performance. Timely identification of nodules is critical for effective treatment and improved survival rates. As a result of advancements in medical imaging technology, CT scans have become the primary approach for identifying lung nodules during the diagnostic phase. The proposed system utilizes an advanced convolutional neural network (CNN) architecture, trained using a vast dataset of annotated lung CT images, to accurately detect and classify lung nodules. The system incorporates various image pre-processing techniques to increase CT scan image quality and improve CNN performance. The suggested system attains notable precision and responsiveness in detecting lung nodules, potentially contributing to the timely identification and management of lung cancer. This report explores a project centered around the detection of lung nodules through the utilization of deep learning techniques.