The escalating demand for automated driving systems underscores the critical importance of effective lane detection technologies. This study scrutinizes the diverse array of machine learning and deep learning algorithms employed in the detection of lanes, focusing on the architectural and segmentation aspects critical for operational success. Employing a comprehensive review methodology, this paper systematically assesses current datasets and the prevailing evaluation metrics used in the field. The methods section delves into various learning techniques, evaluates their efficacy in real-world scenarios, and quantifies their performance in terms of detection accuracy and processing time. Results indicate that while advanced algorithms excel in typical conditions, their performance diverges significantly under adverse lighting and weather conditions. The study concludes by highlighting existing gaps in current methodologies and suggesting avenues for future research, particularly in enhancing the robustness of lane detection systems under varied environmental conditions.