## 1 Introduction  
It is widely known that artificial intelligence (AI) has become an essential addition to industries related to wireless communications. The data obtained from the surrounding environment are huge in volume, as the number of smart devices is increasing--especially in industry, scientific, and medical domains [1, 2]. Connecting rural and distant locations has become crucial for future networks due to the rise in global communication needs and the growth of the Internet of Things [3]. As the variety of smartphones, laptops, tablets, and data-driven sensors are all typical data transceiver devices, their data can be and are processed by AI. The main functionality of AI is to train algorithms to make decisions and take actions. ML is a subset of AI in which large amounts of data are used in training an ML algorithm to allow it to learn more about the processed information [4]. Once the ML algorithm is trained on the training data, it can successfully make predictions or decisions with new data and execute tasks using inferential statistics and arithmetic calculations. This can permit and facilitate ML modeling for wireless communication system availability, mobility accessibility, cross-communication management, and the optimization of automated networks based on 6G data. The aim is to ensure that the key performance indicators are meeting the quality-of-service (QoS) requirements. It is anticipated that the 6G network will offer diverse services and seamless network coverage for everyone and everything. The integrated satellite-terrestrial network design combines the benefits of both satellite and terrestrial networks. The current promises are to provide worldwide broadband connectivity for all sorts of users [5; 6; 7; 8]. It has attracted much interest from both academia and business. A wireless communication system mainly consists of a physical layer, a middle layer, and an end-user layer, as shown in Figure 1. Each layer requires different AI approaches to enhance the QoS, system security, privacy, latency, power allocation and control, and channel capacity [9]. The main concern of the use of ML in the physical layer is the channel encoding, decoding, and estimation. One of these ML applications is scenario classification, which can be regarded as channel estimation. A scenario for a wireless channel is defined as the specific channel environment of data transmission [9]. For example, rural, suburban, urban, and indoor hotspots and satellites are all typical scenarios. The rural macro-cell (RMa) and the urban macro-cell (UMa) are two typical different scenarios. The UMa is deployed in urban areas as it indicates a city or a town, while the term RMa is used for rural areas or countries with smaller populations and less scattering. In some cases, a number of circumstances or scenarios regularly occur. Users that use high-speed transportation, such as high-speed railways, may attest to this. As the user navigates across a variety of possible scenarios, including deserts, mountains, stations, and other impediments, the user places substantial strain on existing communication systems. Therefore, accurately defining wireless channel scenarios is important for meeting user QoS needs. However, the traditional forms of statistics in radio propagation, such as the Okumura and Hata models, were introduced under the assumption that the propagation model of the scenario was previously known, such as in urban scenarios [10]. This can lead to certain inaccurate classifications or mistakes. In response, minimizing complexities and run times and precisely identifying the scenario task are crucial for increasing communication system reliability. Moreover, the power efficiency, beam management, maintenance, bandwidth allocation efficiency, network setup, operation, throughput, QoS prediction, and coverage performance can be tackled by AI-based solutions. Deep-learning methods are usually used in solving complex problems, but require more computational time.