The changes in clouds are complex and diverse, playing a significant role in weather forecast and disaster warning, and affecting our daily lives. The observation of clouds is mainly carried out through radar, remote sensing satellites, and all-sky imagers. The recorded cloud images are divided into radar cloud images, satellite cloud images, and ground-based cloud images, all of which are indispensable parts of cloud observation. With the development of machine learning in multiple fields, it has gradually been applied to cloud segmentation and has made great progress. Through extensive research on literature and achievements in related fields, machine learning cloud segmentation was divided into three types: cloud segmentation methods based on neural networks, cloud segmentation methods based on transfer learning, and cloud segmentation methods based on lightweight models. The methods proposed in recent years for each type were compared, and improvement methods for different problems in cloud segmentation were further summarized. Several improvement schemes were provided for reference.