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Lung cancer is the leading cause of cancer-related death in this generation and is projected to remain so for the foreseeable future. Lung cancer treatment is possible if the symptoms of the disease are detected early. Estimating motion using traditional techniques is challenging for humans. Medical imaging devices are essential for the early detection of lung cancer and the monitoring of lung cancer during treatment. Because of this, some machine learning algorithms can provide efficient, fast, low-error predictions on uncertain raw data. A deep learning (DL)-based convolutional neural network (CNN) approach is proposed to build an effective model to identify high-risk individuals with lung cancer for early intervention to avoid long-term complications. Initially, the dataset images were collected from the standard repository. Then, an image preprocessing stage begins to reduce noise and unbalanced data from the image dataset. The second stage is the segmentation step, that is, to segment each image feature according to the threshold algorithm. Finally, Classification based AI based CNN algorithm evaluating the risk factors uses the proposed model and uses each feature obtained by the last fully connected layer of the model as the input of the activation function. A sustainable prototype model of lung cancer treatment can be created using recent advances in artificial intelligence without negatively impacting the environment. Save time and money by reducing wasted resources, effort and time needed to complete manual tasks. A combination of AI-based CNN layers yielded an accuracy of 98.52%. The proposed model is a stable and consistent diagnostic model for the detection and diagnosis of lung cancer.