Abstract: Due to the high cost and low efficiency of field investigation, unmanned aerial vehicle (UAV) remote sensing has been applied to marine litter monitoring worldwide in recent years. To evaluate the effectiveness of UAV remote sensing and machine learning in marine litter monitoring, firstly, we compared visual interpretation result with those obtained from field investigation. The results showed that the overall recognition precision of marine litter reached 68.1%~75.1%, while the recognition precision of all kinds of plastic litter was 38.9%~94.3%, proving the reliability of image interpretation. Secondly, based on the visual interpretation of litter remote sensing images, machine learning method was introduced to image interpretation. The recognition precision of various types of plastic litter reached 77.9%~81.5%, and the recovery rate was 45.6%~60.6%. Finally, this method was successfully applied to analyze the flux, composition and spatial distribution of plastic litter in Chongming Island and to reveal the differences between before and after the typhoon. In brief, we developed and verified the application value of UAV remote sensing and machine learning to detect marine macro-plastic litter. We realized the complete monitoring process of semi-automatic data collection, identification and statistics, which improved the monitoring efficiency. Machine learning model developed in this paper can be used to solve the demand of litter location and identification in massive UAV images. This intelligent monitoring and evaluation method can support further scientific research and management of marine macro-plastic litter in coastal zone.