Lung cancer becomes critical to human wellness because of its fast-rising rates of disease and loss of life. As a valuable method for increasing the diagnosis precision and detecting cancer earlier, low-dose computed tomography (LDCT) assessment is developed, which leads to decreasing death rates. Designing a computer-aided detection (CADe) method is of great medical significance when using CT data because of its unpredictable nature. Such system would benefit radiologists by helping them to automatically analyze nodules, increasing accuracy of their diagnoses, and enabling prompt patient interventions. An outline of artificial intelligence (AI) systems for pulmonary nodule detection is presented in this work. Findings show the increasing usage of machines and deep learning methods, demonstrating their effectiveness in detecting, classifying, and are tested in. This study confirms that AI helps identify and analyze lung cancer and reduces exposure. With remarkable advances in deep learning, medical imaging studies have occurred as a major research field. This paper provides a broad review of the richness of lung image evaluation by means of deep learning techniques, focusing on different pattern recognition tasks such as classification and local/identification, distribution, and registration.