of AI into communication networks, where it offers potential solutions for optimizing network resources, enhancing security, and predicting traffic patterns [7]. For instance, machine learning models can dynamically manage bandwidth allocation to reduce latency and improve the Quality of Service (QoS), while deep learning models can identify and mitigate potential security threats by detecting anomalies in network traffic [8].  
In modern communication networks, the applications of AI are vast and varied [9]. AI-driven traffic prediction enables real-time load balancing, which is essential for maintaining service quality in congested networks [10]. Additionally, AI-based security measures, such as intrusion detection systems, play a critical role in safeguarding networks against cyberattacks [11; 12]. Furthermore, the advent of Self-Organizing Networks (SONs), powered by AI algorithms, facilitates autonomous network management by enabling real-time configuration and fault detection without human intervention [13; 14].  
Although significant progress has been made, implementing AI in communication networks still presents several challenges [15]. Data privacy and security concerns arise from the vast amounts of sensitive data required to train AI models, and the scalability of AI algorithms is constrained by the limited computational resources available in network infrastructure, particularly at the edge [16; 17]. Additionally, the interpretability of AI models remains a significant concern, as network operators require transparency in AI decision-making to build trust and ensure compliance with regulatory standards [18]. These limitations highlight the need for continued research and development to refine AI techniques and address these challenges effectively [19].  
Although AI has demonstrated significant potential in optimizing communication networks, the practical implementation of these models requires a thorough evaluation. Metrics such as latency, energy efficiency, and response times must be quantified to assess their scalability and practicality. Furthermore, comparing AI-driven approaches with traditional rule-based methods highlights their unique advantages and limitations, particularly in resource-constrained environments such as IoT networks.  
This paper aims to provide a comprehensive overview of the applications, challenges, and future directions of AI in communication networks. The main contributions of this work are as follows:  
* I present an in-depth analysis of the various AI techniques, including machine learning, deep learning, and federated learning, applied to communication networks, highlighting their strengths and limitations in different network scenarios.
* I explore key applications of AI in communication networks, such as network optimization, traffic prediction, and security enhancement, and discuss case studies that demonstrate these applications in real-world scenarios.
* I identify the main challenges and limitations associated with AI deployment in communication networks, focusing on issues related to data privacy, scalability, and interpretability.
* Finally, I outline potential future directions for AI in communication networks, including trends like edge AI, Explainable AI (XAI), and AI-driven advancements anticipated in 6G networks.  
The rest of this paper is organized as follows: Section 2 provides an overview of AI techniques commonly used in communication networks. Section 4 delves into specific applications of AI, examining how these methods enhance network performance and security. Section 6 presents case studies that illustrate the practical implementation of AI in modern communication networks. Section 7 discusses the challenges and limitations in adopting AI, and Section 8 offers insights into future directions for research and development. Finally, Section 10 concludes the paper with a summary of findings and implications.