<p>Cancer-related fatalities are predominantly attributed to lung cancer, necessitating early detection for mitigation. Recent advancements in machine learning (ML) and deep learning (DL) have demonstrated potential in enhancing the precision and rapidity of lung cancer detection, especially through the analysis of medical imaging techniques such as CT scans and X-rays. This paper offers a thorough analysis of contemporary research publications examining the application of machine learning and deep learning techniques for lung cancer detection. The paper examines multiple approaches, encompassing conventional machine learning techniques such as support vector machines (SVM) and random forests, with deep learning frameworks including convolutional neural networks (CNN) and recurrent neural networks (RNN). The research examines the benefits, constraints, and performance metrics of these models regarding accuracy, sensitivity, and specificity, emphasising hybrid models and feature engineering methodologies. Moreover, the research underscores significant hurdles in the domain, including the necessity for more extensive, annotated datasets and the potential for minimising false positives. This research synthesises findings from multiple studies to offer insights into advanced approaches and future possibilities for lung cancer diagnosis utilising machine learning and deep learning.</p>