## 1 Introduction
### Related Work  
As we close in on the unveiling of the 6G network, researchers are exploring different aspects of the future network and its interaction with AI/ML technology. [PERSON] et al. [29] explored the impact of Swarm intelligence on next-generation networks. The authors focused on the application of SI for spectrum management and resource allocation, caching and edge computing, and network security for 5G and beyond networks. However, the studies did not investigate the use case for ML approaches. [PERSON] and [PERSON] [30] reviewed deep and reinforcement learning approaches for next-generation wireless networks. The authors focused mainly on the use cases for physical layer security (PLS) applications. Similar studies with discussions on ML implementation for different 6G networks, ranging from vehicular networks, device-to-device networks, and Fog-radio access networks, have also been published [16; 22; 31; 32; 33; 34; 35]. Although the authors explored use cases towards 6G, little or no discussion was conducted on 6G supporting technologies. [PERSON] et al. [22] discussed in detail an overview of the challenges and potential advantages related to integrating ML techniques with advanced technologies in 6G networks; however, the authors did not expand on the optimization aspect of wireless resources management to support ML applications. Similarly, [PERSON] et al. [33] reviewed the role of ML at the application and infrastructural level of 6G network. [PERSON] et al. [34] focused on ML/AI approaches for 6G networks, but the authors considered only three major application areas of 6G, these include dynamic spectrum allocation, mobile edge computing, and ITS. This study is limited in the sense that it only addressed a limited aspect of 6G communication. Studies that specifically focus on exploring the integration of ML with key 6G enabling technologies have received considerable attention. Several authors have covered a range of reviews focusing on integrating ML with notable key technologies. Some of these studies covered aspects such as quantum computing and machine learning integration (QML) [4; 36; 37], intelligent reflective surfaces (IRSs) and ML [15; 38; 39], ultra-reliable and low-latency communications (URLLCs) and ML [40; 41], and cell-free MIMO and DL [42]. While most of these studies have explored the potential of integrating ML with different enabling 6G technologies, they are limited in that they only considered a dedicated technology in their study. How ML enables other emerging 6G technologies has not been explored or covered. For the other reviews, the focus has been on a specific ML approach, for example, FL [43; 44; 45], deep learning approach [21; 30; 46; 47; 48; 41; 42], or distributed machine learning (DML) approach [49; 50]. These authors did not consider other ML approaches and thus limited their survey. On the other hand, ML approaches have also been reviewed to address issue-specific scenarios like for privacy and security concerns in 6G networks [16; 43; 49], sensing and localization [51; 52], and network access optimization [53; 54]. However, only [PERSON] et al. [15] discussed extensively several emerging supporting 6G technologies and how ML can be leveraged for their optimum functioning. However, the authors only considered a limited number of these technologies. Other key technologies such as blockchain and digital twin were not highlighted. Furthermore, discussions of the related published reviews were limited to studies published between 2021 and 2023. The authors only provided a narrowed overview of the existing literature and briefly discussed the 6G network evolution. Consequently, there exists no in-depth survey that explores wireless network evolutions, 6G network concepts, architecture, enabling technologies, services, and ML integration with the different enabling technologies of 6G networks. So, in this paper, we address all the concepts and convergence for optimized 6G performance in detail.