This survey provides a comprehensive review of the methods used for lung cancer detection through thoracic CT images, focusing on various image processing techniques and machine learning algorithms. Initially, the paper discusses the anatomy and functionality of the lungs within the respiratory system. The review examines image processing methods such as cleft detection, rib and bone identification, and segmentation of the lung, bronchi, and pulmonary veins. A detailed literature review covers both basic image enhancement techniques and advanced machine learning methods, including random forests (RFs), decision trees (DTs), support vector machines (SVMs), K-nearest neighbors (KNNs), artificial neural networks (ANNs), convolutional neural networks (CNNs), and gradient boosting. The review highlights the necessity for reliable validation techniques, explores alternative technologies, and addresses ethical issues associated with the use of patient data. The findings aim to assist researchers and practitioners in developing more accurate and efficient diagnostic tools for lung cancer detection by providing a concise review, thereby helping to save time and focus efforts on the most promising advancements.