## 1 Introduction  
The emergence of the fifth-generation (5G) mobile network represents a significant milestone in the field of telecommunications. This advanced technology promises to revolutionize connectivity, ushering in a new era with the potential for transformative impacts on various sectors [1]. As 5G networks provide ultra-high-speed data transmission, minimal latency, and high-density connectivity, they are poised to underpin crucial developments in the Internet of Things (IoT), autonomous vehicles, smart cities, and other technological advancements [2]. As 5G networks increasingly underpin essential sectors such as healthcare, transportation, defense, and public services, robust security measures become paramount to sustain trust, safeguard privacy, and ensure the functional integrity of these sectors [3]. With 5G infrastructure becoming a cornerstone of our digitally oriented society, the intricate and pervasive nature of its security implications necessitates thorough scholarly exploration. Recent research has highlighted the centrality of 5G security in sustaining the reliability and integrity of 5G services [4]. The susceptibility of 5G networks to cyberattacks, both from known and unidentified sources, has been a focal concern [5]. The necessity for dynamic and adaptive security solutions has been pointed out by several scholars, emphasizing the need for innovative approaches to combat evolving cyber threats [6]. While conventional methods of cybersecurity have been effective to a degree, the exponential increase in data volumes, device heterogeneity, and system complexity inherent in 5G networks necessitate the exploration of more innovative, dynamic, and adaptive security measures. To this end, artificial intelligence (AI) branches such as machine learning (ML) and deep learning (DL) have been increasingly recognized as pivotal in strengthening 5G security [7]. Leveraging the power of these AI subdomains is critical in examining their promising roles in fortifying 5G networks against a myriad of potential cyber threats. ML, with its ability to learn and improve from experience, provides a robust framework for identifying patterns, detecting anomalies, and predicting potential threats, thereby enhancing the effectiveness and efficiency of security solutions [8]. On the other hand, DL, a subset of ML that imitates the functioning of the human brain in processing data, offers a deeper layer of security by recognizing and mitigating complex cyberattacks in real time [9]. The pivot to 5G is not just a technological shift; it is an evolutionary leap that brings unique challenges, many of which remain uncharted in current research, like big data [10]. Using ML and DL, the promise these techniques hold for security is yet to be fully harnessed, especially tailored for 5G's dynamic environment. Given our escalating dependence on 5G infrastructures and the palpable risks of security oversights, there is a pressing imperative to delve deeper, to preemptively identify vulnerabilities, and to devise robust countermeasures. This research delves into these areas, shedding light on how ML and DL can be leveraged to ensure 5G security. Also, it seeks to fill this void by providing a comprehensive examination of 5G security, tracing its evolution, dissecting the associated threats, and probing the effectiveness of current and prospective security solutions. Furthermore, it explores the role of ML and DL in various areas of 5G security, including intrusion detection, risk prediction, security protocol optimization, and more. It also aims to provide a holistic perspective on the potential of ML and DL in enhancing 5G security. It also intends to address the challenges and limitations encountered in integrating these advanced technologies into 5G networks and offers potential solutions in this exciting area of research. This research, therefore, embarks on an exhaustive exploration into the world of 5G security, with a keen focus on the revolutionary potential of ML and DL.