Due to its rising death rate, lung cancer is the most common cause of cancer-related fatalities globally, making early detection crucial for raising survival rates. However, the limited number of radiologists and the surge in image data affects accurate evaluation, prompting researchers to develop automated methods for predicting cancer cell growth using medical imaging. This review article provides a comprehensive survey of 25 research papers on lung cancer detection and classification techniques, including Genetic Algorithms, Convolutional Neural Networks (CNN), Transfer Learning (TL), Support Vector Machines (SVM), Optimization, 3D CNN, Neural Networks (NN), and other Deep Learning (DL) approaches. The analysis highlights the advantages and disadvantages of each approach by classifying research methodologies, publication years, datasets, and performance measures. Despite the advancements, several drawbacks and research gaps exist, including the need for improved dataset diversity, enhanced model interpretability, and addressing class imbalance and overfitting issues. This review identifies these gaps and issues, providing motivation for developing effective methods to enhance lung cancer detection and classification. By tackling these issues, scientists may develop more dependable and accurate models, which will ultimately lead to better patient outcomes and even save lives. This survey acts as a basis for next investigations, directing the creation of novel strategies to combat lung cancer.