## 1 Introduction  
The light detection and ranging (lidar) technique has emerged as a critical tool for understanding the Earth's atmosphere. It enables retrieval with high temporal and spatial resolution of the vertically resolved optical, microphysical, and geometric properties of aerosols [1, 2] and clouds [3, 4], which play a key role in modulating the Earth's radiation budget [5, 6], influencing climate change, and affecting air quality [7, 8, 9, 10]. Figure 1 shows the main domains in which lidar technology plays a crucial role in atmospheric studies. Aerosols and clouds are dynamic entities that constantly change in response to various environmental and anthropogenic factors. Their intricate relationships with radiation, weather patterns, and climate processes [11] require the use of vertically resolved high-resolution atmospheric profiles. Moreover, capturing data at a high temporal resolution allows for detailed and timely observations of atmospheric changes. Such comprehensive insights are uniquely offered by technologies such as ground-based lidar. On the contrary, lidar instruments deployed on satellite, aircraft, and ground-based platforms are capable of providing near-continuous vertical profiles (with a much longer revisit time) of aerosols and clouds [12, 13], allowing researchers to gain an unprecedented view of their structure, density, and distribution. However, while lidar technology continues to evolve and improve, the complexity and sheer volume of data it generates present considerable challenges. The heterogeneity and dynamic nature of aerosols and clouds make the retrieval of their properties a complex task. Here, machine learning comes into play. Machine learning, a subset of artificial intelligence [14; 15], can handle large datasets and unravel intricate patterns within them. It offers robust methodologies for automating the extraction of meaningful information from complex data. Machine learning algorithms can extract valuable insights from lidar observations, to characterize and detect cloud and aerosol layers. This aids in their parameterization for climate and weather forecasting models and advances our understanding of their impacts on climate and air quality. In the past two decades, the literature has witnessed numerous classic approaches, but in recent years, analysis through machine learning algorithms has presented a promising path toward a better and more comprehensive understanding of aerosol and cloud dynamics, as well as their broader environmental implications. In this survey, the author analyzes the state of the art of the principal machine learning and artificial intelligence (AI) techniques applied to lidar observations to obtain aerosol and cloud properties. This survey is primarily aimed at students and professionals seeking to deepen their understanding of the topic, providing a detailed exploration of the fundamental aspects of machine learning applied to lidar data analysis, and categorizing the approaches into three main classes. The first class revolves around the detection and classification of aerosols, clouds, and precipitation. The second class focuses on detecting mixing layer height (MLH) and planetary boundary layer height (PBLH) by incorporating machine learning and deep learning approaches borrowed from computer vision. Hybrid approaches that combine different philosophies have also been considered within this category. This survey aims  
Figure 1: Different lidar techniques and relative detection/classification of aerosols, clouds, and precipitation problems where machine learning algorithms can play a key role. to change perspectives on quality assessment, by encouraging the application of newly developed approaches in real-world environments. In doing so, the aim is to outline new directions for future research in this domain. In conclusion, this survey aims to provide a comprehensive overview of the machine learning technologies applied to atmospheric physics lidar observations.