Our project AI DR Assistant aims to create a group of artificial intelligence models who help in implementation early diagnosis and detection of cancers, and who contribute to helping the patient and doctors. In order for this project to work, we went through several stages, starting with us empathizing with the people who face these problems and the doctors, identifying the main problems, proposing the appropriate solution, and working on developing this solution to the testing stage. It is important to note that traditional methods, such as chest x-rays, have an error rate of approximately 90%, a rate high enough to cause complications. In some cases, lung cancer is misdiagnosed as pneumonia, delaying treatment and increasing the risk of death.so We developed an advanced AI system to improve lung cancer diagnosis, using the latest machine learning techniques, including unsupervised learning and convolutional neural networks (CNNs). The model was trained on a dataset of 112,000 chest x-rays from the National Institute of Health, with the goal of improving early detection rates and reducing human error in radiological interpretations. The system demonstrated a 75% diagnostic accuracy, reflecting its effectiveness in analyzing x-rays and identifying early signs of lung cancer. The results demonstrated the model's ability to support physicians in making immediate decisions, reduce delays in diagnosis, and potentially reduce mortality rates associated with late-stage detection.