Maintenance and assessment of runway pavement conditions are vital to ensure airport safety. Traditional methods of runway inspection, which often rely on manual surveys and visual assessments, can be time-consuming, labor-intensive, and prone to errors. In recent years, the integration of unmanned aerial vehicles (UAVs) equipped with LiDAR technology has emerged as a promising alternative for improving the accuracy and efficiency of pavement damage detection. This review paper provides a comprehensive analysis of the current state of research on the use of low-cost drone-based LiDAR systems for runway surface assessment. The review explores key advancements in the development of pavement damage assessment models, including methods for detecting surface irregularities, quantifying damage severity, and classifying various types of distress such as cracking, rutting, and surface deformation. Additionally, the paper discusses the advantages of this approach over traditional methods, such as faster data collection, cost-effectiveness, and enhanced spatial accuracy. Challenges related to data processing, system integration, and regulatory compliance are also highlighted. By synthesizing recent findings and identifying research gaps, this review aims to provide insights into the potential of low-cost drone LiDAR technology to revolutionize runway maintenance practices. The paper concludes by outlining future research directions, particularly in the areas of automated data processing, real-time damage detection, and system scalability for larger airport infrastructure.