## 1 Introduction
### Key Contributions  
A substantial number of reviews and surveys exploring ML applications to 6G networks have been intensely covered in recent years. However, in contrast to the already published surveys, our study narrows on ML prospects when integrated with several key emerging technologies focused on the future 6G networks and beyond. It covers the integration of several complementary 6G technologies with ML for optimized 6G network performance. In this survey, we have performed the following:  
* Discussed the evolution of wireless networks and their development into 6G communication. We provided an in-depth overview of 6G concepts, architecture, enabling technologies, features, and applications. * Provided a comprehensive overview of the existing related reviews spanning 6 years (2019-2024) detailing their titles, year published, reviewed literature spans, area of focus, merits, and shortcomings as compared to ours. * Discussed different ML techniques, their advances in the wireless communication network domain, and other emerging applications. ML capabilities are highlighted and its role in the future 6G network. * Discussed key enabling technologies, their individual roles in the incoming 6G network, and their integration with ML for optimization approaches towards the expected technological enhancement that will enable the 6G communication. * Highlighted some open challenges and future research directions: Potential challenges and future research directions toward achieving the 6G target have been extensively discussed. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**References** **(Year)** & **Review** **Span** & **Focal Area** & **Discusses** **6G** & **Discusses** **ML** & **Discusses** **ML** & **Intersection with 6G** & **Discusses** **Future** **Research** \\ \hline
[4] & 2019-2021 & The focus is on quantum computing integration with ML for 6G wireless network applications. & Yes & Yes & No & Yes \\ \hline
[16] & - & Focuses on ML application for addressing privacy concerns in 6G networks. & Partially & Partially & No & No \\ \hline
[34] & 2018–2020 & Focuses on AI/ML role in three major 6G applications, specifically, dynamic spectrum allocation, mobile edge computing, and ITS. & Yes & No & No \\ \hline
[55] & - & Focuses on the use of ML for the design and optimization, channel measurements, modeling, estimation, physical layer research, and network management of B5G networks. & No & No & No \\ \hline
[56] & - & Focuses on ML techniques applied to communication, networking, and security parts in 6G vehicular networks. & No & No & No & No \\ \hline
[53] & - & Focuses on ML application for end-to-end network optimization for 6G vehicular network. & No & Yes & No & Yes \\ \hline
[40] & 2019 & Focuses on ML application to ultra-reliable and low-latency communications (URLLCs) in 6G. & No & Partially & No & No \\ \hline
[31] & 2019–2021 & Focuses on DRL approaches for vehicular networks. & No & Partially & No & No \\ \hline
[33] & 2019–2021 & Focuses on the role of ML techniques in each layer of the model for 6G networks. & Partially & Yes & No & No \\ \hline
[41] & 2017–2020 & Focuses on DL integration with URLLC for 6G networks. & Yes & Partially & No & Yes \\ \hline
[57] & 2017-2020 & Focuses on ML application for the handover management of & No & Yes & No & Yes \\ \hline
[58] & 2021 & Focuses on ML for network application in 6G networks. & Yes & Yes & No & Yes \\ \hline
[59] & 2017-2021 & Focuses on the integration of ML, energy, and & Yes & No & No & No \\ \hline
[47] & 2017-2021 & Focuses on DL application to different 6G architectural layers. & No & Partially & No & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of related surveys and their area of focus as compared to ours.