[15] primarily focuses on deep learning techniques rather than general ML techniques. Moreover, while [14; 15] discuss open problems and future research directions, they primarily look at these from the perspective of O-RAN, rather than looking at the specific challenges and opportunities associated with ML-empowered O-RAN. Different from the existing surveys, in this article, we dive deep into the ML integration in O-RAN, covering recent research works that study the application of ML in O-RAN, discussing emerging issues and research opportunities that shall be addressed to fulfill its design commitment. We believe this survey marks a significant milestone as the first comprehensive endeavor aimed at summarizing recent studies and providing crucial technical guidance to researchers interested in ML-enabled O-RAN. Within this paper, we also present research opportunities across diverse areas encompassing data collection and analysis, as well as the development, deployment, maintenance, and operation of ML within the O-RAN domain. The structure of the remaining sections in this paper is as follows. Section 2 provides an overview of the O-RAN architecture and its development, highlighting its design principles that support network automation. In Section 3, we survey the existing ML applications in the context of O-RAN. Section 4 focuses on the potential of O-RAN and discusses the research opportunities for applying ML techniques to enhance various aspects of mobile network operation within the O-RAN framework. Finally, Section 5 concludes the survey and discussion presented in this paper. We illustrate the organization of this paper in Figure 1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline Year Ref & & & & & & \\ \hline
2023 & [1] & H & M & M & This paper provides a detailed tutorial on O-RAN, describing its architecture, design principles, and the O-RAN interfaces. \\ \hline
2021 & [4] & H & L & L & This paper overviews the idea of O-RAN and presents ongoing O-RAN Alliance standardization activities in this context, followed by a study of traffic steering use case. \\ \hline
2022 & [12] & H & L & L & This paper provides a comprehensive survey of O-RAN development, encompassing a summary of the RAN evolution history, an introduction to O-RAN technology, an overview of Open RAN-related projects and activities, a discussion of standardization efforts, challenges, and potential solutions. \\ \hline
2021 & [13] & M & L & L & This paper provides an overview of the O-RAN Alliance RAN architecture, highlighting its core building blocks and, subsequently, it presents a practical use case that leverages AI/ML-based innovations. \\ \hline
2022 & [14] & H & M & M & This paper presents an O-RAN architecture overview, delves into AI applications within Op-RAN, and discusses the challenges and opportunities in implementing intelligent solutions in 5G and B5G telecommunications. \\ \hline
2022 & [15] & H & M & M & This paper focuses on mapping existing deep-learning-based studies to the O-RAN architecture, highlighting key technical challenges, open issues, and future AI-enabled O-RAN research directions. \\ \hline
2023 & [16] & M & L & L & This paper examines security and privacy risks in O-RAN architecture, proposes possible solutions and presents relevant security standardization efforts. \\ \hline
2023 & [17] & M & L & L & This paper focuses on Non-Terrestrial Networks exploring the possible implementation of an O-RAN-based NTN solution. \\ \hline
2022 & [18] & H & L & L & This paper identifies critical limitations in current O-RAN specifications: security, latency, real-time control, and AI-based RAN control. \\ \hline
2023 & [19] & H & M & L & This paper focuses on XAI methods and explores their deployment within the context of O-RAN. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of surveys relevant to ML-enabled O-RAN. H: High, M: Medium, and L: Low.