Lung cancer represents a significant contributor to global cancer-related deaths, underscoring the critical need for early detection to reduce mortality rates. Using convolutional neural networks (CNNs) and deep learning, with a specific focus on MobileNet, VGG16, GoogleNet, InceptionentV3 and ResNet50, this study delves into the integration of AI for lung cancer detection using the LC25000 dataset, encompassing a diverse range of lung pathology CT scans. By tailoring the MobileNet architecture and optimising it for CT image analysis, the research strives to enhance the model’s precision in identifying lung malignancies. The customized MobileNet, InceptionNet, GoogleNet, ResNet50, VGG16 model undergoes fine-tuning via strategic adjustments and training to discern subtle patterns indicative of lung cancer. Ensembled with these models to give accurate results. within medical imaging datasets. Robust evaluation techniques are implemented to gauge the model’s efficacy, incorporating metrics such as accuracy and computational efficiency, positioning it as a promising tool for advancing early lung cancer detection methodologies.