## 1 Introduction  
Wireless communication systems are changing dramatically as a high data rate and quality of service are in soaring demand. Fifth generation (5G) wireless communication has already been deployed and is currently in service in many countries [1]. Therefore, sixth generation (6G) wireless communication has received full attention from the researchers. The 6G wireless networks include an ultra-high data rate, high reliability, global coverage, low latency, high energy efficiency, and high reliability [2]. To meet these requirements, we need more advanced network devices and new techniques for efficient wireless communication. The literature published in recent years suggests that, for 6G, the main ideas are terahertz communication, artificial intelligent and intelligent reflecting surfaces (IRSs), or reconfigurable intelligent surfaces [3, 4]. IRS is considered a new paradigm for wireless communication in the 6G [5]. IRS is a thin metasurface made of a dielectric material capable of reflecting electromagnetic (EM) waves in an organized way. Thus, IRS is considered to be a software-controlled smart radio environment that can mitigate multipath problems and is effective for millimeter wave or terahertz communication [6]. In recent years, the application of IRS devices has dramatically increased, owing to its excellent convenience in wireless communication, such as mobile edge computing, simultaneous wireless information and power transfer, enhanced physical layer security, device-to-device communication, mmWave massive multiple-input multiple-output (MIMO), unmanned aerial vehicles communication for smart cities, and intelligent internet of things (IoT) applications for wireless sensor networks [7]. In the state-of-the-art, many studies have been conducted on the design of IRS-aided communication for the different performance matrices [8; 9; 10]. The study conducted in IRS can be categorized as cascade channel estimation, phase shift optimization, beamforming optimization, multiuser communication, and reflecting metasurface grouping [11; 12; 13; 14]. Machine learning (ML) methods are considered as a revolutionary technology of the branch of artificial intelligence (AI) [15]. ML methods can learn from data and make predictions for future events. Recently, ML-based approaches have been widely adopted in wireless communication technologies [16; 17; 18; 19]. IRS-based communication has also adapted different ML techniques for performance enhancement. The categories of ML applied in IRS are classified as deep learning (DL), reinforcement learning (RL), supervised learning (SL), unsupervised learning (UL), and federated learning (FL). Results in the previous studies demonstrated that ML-based methods have a comparable performance with the conventional methods while reducing the computational complexity [16; 20]. Wireless communication technologies can be revolutionized by the power of ML. Recently, deep learning (DL)-based IRS technology can help to better extract the inherent relationship between the input-output signals and achieve a more reliable channel estimation, as shown for OFDM and mmWave massive MIMO [21; 22] compared to the conventional or traditional model-based approaches. A DL-based channel estimation for an IRS-aided system was proposed in [23], where the authors proposed a single IRS system while considering MISO-OFDM. In [7], convolutional neural networks (CNN) for estimating the direct and cascaded channels sequentially were proposed. However, this design may suffer from the error propagation issue, since the estimated direct channel is used to construct the estimated cascaded channel. In the literature, some survey papers covered IRS-based technologies [7; 24; 25; 26], [PERSON] et al. [7] provided fundamental principals of IRS communication and interactions with electromagnetic signals. Next, the authors surveyed a performance analysis of the multi-antenna assisted IRS system, and beamforming and resource allocation techniques.