Abstract: In response to the urgent need for monitoring low-altitude unmanned aerial vehicles (UAVs), acoustic detection technology offers a unique physical sensing dimension for drone surveillance in complex low-altitude environments, owing to its passive nature, all-weather adaptability, and cost-effectiveness. This paper provides a comprehensive review of the research progress in this field, both domestically and internationally, following a technical framework that encompasses “source characteristics, propagation mechanisms, localization methods, and system implementation”. Firstly, the mechanisms of airborne and mechanical noise generation in UAVs, the corresponding modeling methods, and their harmonic characteristics are discussed. Secondly, the special effects of atmospheric scattering, absorption, meteorological conditions, and terrain on acoustic propagation in low-altitude environments are analyzed, along with the related modeling techniques. Subsequently, the principles, performance, and applicable scenarios of acoustic localization algorithms based on time-of-arrival differences, beamforming, distributed networks, and deep learning are compared and evaluated. Finally, the engineering developments of typical acoustic detection systems are presented, and the current fundamental theoretical and technical challenges are identified. Based on these findings, the paper outlines future trends in the integration of physical models with data-driven approaches, as well as the collaboration of intelligent arrays and multiple modalities for more advanced systems.