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Cancer of the lung is among the major causes of death induced by cancer in the world, and this is caused by delayed diagnosis and quick advancements. Uncovering early has great impact on the treatment and survival likelihood. In this paper, an effective deep learning-efficient prediction model to detect lung cancer disease is proposed to demonstrate the effectiveness of the use of convolutional neural networks (CNNs) to carry out a precise and automated lung illness acknowledgement utilizing medical images. On the one hand, the model is trained and validated on a publicly available lung CT scan dataset, where the use of advanced preprocessing methods to increase image quality and feature extraction is applied. Compared to the classic machine learning methods, the suggested system is more accurate, sensitive, and specific in its work. This model provides an encouraging approach to lung cancer detection that is both reliable and early because it considers the use of deep learning as part of the diagnostic process.