## 1 Introduction  
Efficient design strategies for wireless communication systems are essential for communication networks' fifth and sixth generations. Service requirements for 5G/6G network, such as high data traffic, throughput, latency, availability, energy efficiency, and cost-efficiency, make an efficient implementation of applications such as vehicular communication, Industry 4.0, and the Internet of things (IoT) possible [1, 2]. In contrast to 4G networks, 5G applications offer native support to applications-oriented communications between machines [3]. On the other hand, recently, research has been focused on 6G wireless communication systems, adding higher reliability and ultra-high data rates. The design combines simultaneous communication and sensing to detect objects' presence and identify their shape, location, and speed [4, 5, 6]. Moreover, 6G networks will be designed to be self-protected, self-planned, and will self-manage their resources despite energy constraints [7]. To achieve these goals, new transmission technologies have been proposed; among others, reconfigurable intelligent surfaces (RISs) are considered one of the key technologies in 6G communication systems. RIS-assisted wireless systems can tune the wireless propagation environment, making a smart radio context [8]. A RIS is also known as an intelligent reflecting surface (IRS) or a large intelligent surface (LIS). This technology is a two-dimensional surface comprising controllable elements of low-cost passive components. The most important property of an RIS is its capability to modify dynamically the wireless communication environment [9]. Due to the aforementioned properties, RISs are raising interest for a variety of applications such as power transfer, physical layer security [10; 11], unnamed aerial vehicles communication [12; 13], IoT and sensor networks [14], non-orthogonal multiple access (NOMA) [15; 16], spatial modulation (SM) [16; 17], among others. In 6G communications, the RIS has become a leading technology for mmWave communications [18; 19]. On the other hand, machine learning (ML) is a technology derived from artificial intelligence (AI) that focuses on extracting knowledge from data (learning) to obtain patterns that design the rules or methods to make predictions, which allow interactive and smart decisions to estimate parameters efficiently. ML has been successfully applied to computer vision, signal, and language processing tasks. Due to its capabilities, ML has been considered a powerful technique to assess wireless network performance. This can be done by learning in almost real time the relevant features of a physical communication environment [20]. The intersection of ML and wireless communication systems has become a prolific field of research and an alternative for the design of future-generation networks [21]. Next-generation networks will have higher levels of complexity due to their higher data rates, self-configuration, and self-management capabilities. In this scenario, ML-based wireless communications can be designed without relying on complex mathematical models [22]. Recently, the research on ML methods applied to wireless systems has gained notable attention. The effectiveness of learning algorithms enhances the system performance, reliability, and adaptability of networks, where predictions bring better decisions in real-time systems [23]. Most research, surveys, and tutorials focus on RIS fundamentals and operating principles in the state of the art. In contrast, other surveys exist ML applications for RIS-aided networks [24; 25; 26; 27; 28; 29; 30]. In [24], the authors focused their review on DL-based schemes to enhance RIS communications. They characterize a taxonomy of ML RIS systems to summarize the algorithms applied, such as supervised learning (SL), unsupervised learning (UL), federated learning (FL), and reinforcement learning (RL). The paper states the ML model architecture, the major contributions, and remarks for each revised research framework.