Globally, lung cancer consistently ranks among the top causes of cancer-related fatalities. Detecting it early and accurately is vital for successful treatment and enhanced patient survival. In this research, we present "DeepLung," an advanced Convolutional Neural Network (CNN) model tailored specifically for predicting lung cancer through medical imagery. By tapping into the capabilities of deep learning, DeepLung can autonomously and effectively identify features in complex data, bypassing the need for hand-picked feature extraction. Our dataset, which consists of thousands of annotated lung images from various demographics, underwent thorough preprocessing to maintain data uniformity. When trained, validated, and tested on this data, DeepLung outperformed conventional diagnostic techniques, boasting superior accuracy and fewer false positives. Additionally, we employed cutting-edge regularization strategies, enhanced data sets through augmentation, and incorporated transfer learning to fine-tune our model, ensuring its reliability and adaptability in different medical settings. With its potential application in real-world settings, DeepLung could serve as a supportive tool for medical practitioners, particularly radiologists. Ultimately, our findings underscore the revolutionary role of CNNs in the realm of medical diagnosis, setting new standards for early and precise lung cancer detection.