(UDNs) [1; 2]. Modern users generate an unprecedented volume of data traffic, necessitating a fundamental shift in network design. Emerging services and applications, such as high-definition video streaming, augmented reality, cloud computing, and the IoT, impose stringent performance requirements. Compared to 4G, 5G cellular networks support up to 100 times higher throughput, primarily through aggressive cell densification using femtocells, picocells, and microcells. However, despite the maturity of the 5G standard, several critical challenges remain [3; 4]. In parallel, global efforts are underway to define the 6G standard, which targets ultra-low latency, extreme data rates, and integration with AI-native services [5; 6]. Sixth-generation cellular networks aim to support new services with extremely stringent requirements, including ultra-low latency, extreme data transfer rates, and enhanced AI-enabled applications. The sheer complexity of 6G has garnered global research interest, with major contributions from research teams in China, South Korea, the United States, and Europe. Despite the growing attention on 6G, significant research efforts continue within the 5G domain, particularly in relation to enhancing network performance, scalability, and efficiency. This paper reviews recent publications focused on mobility and resource management in ultra-dense 5G and 6G cellular networks. Special attention is given to handover management, which becomes increasingly complex in UDNs due to more frequent inter-cell transitions. Frequent handovers may result in ping-pong effects and degraded performance due to increased signaling overhead and handover failures. These issues are particularly critical for mobile devices, which are sensitive to energy consumption [7; 8]. Additionally, interference management is a pressing concern, as dense cell deployments height co-channel interference. Traditional mitigation techniques, such as power control and frequency reuse, must be enhanced through AI-driven predictive modeling to ensure reliable connectivity. Load balancing mechanisms are equally vital, as they efficiently distribute user traffic to prevent congestion, which can affect the quality of service (QoS) [9; 10]. Load balancing strategies in wireless networks aim to distribute traffic and user connections evenly across available base stations or network resources to prevent congestion [11], reduce latency, and enhance QoS. Traditional methods include cell range expansion, handover thresholds, and resource-aware scheduling. With the increasing complexity of heterogeneous and ultra-dense networks [12], AI-based approaches have emerged as effective solutions. These approaches utilize machine learning (ML) and deep learning (DL) techniques such as deep reinforcement learning (DRL), federated learning, and edge intelligence to make dynamic, real-time decisions for optimal resource allocation and traffic distribution. AI models can learn from historical and real-time network data to predict congestion patterns, anticipate user mobility, and proactively balance loads across the network [13]. UAVs are anticipated to play an integral role in future mobile networks, contributing to the vision of self-organizing networks. UAVs will be deployed both as mobile users and airborne base stations, offering remarkable versatility and adaptability. Their increasing popularity stems from their flexibility, low cost, and ease of deployment, making them promising candidates for enhancing 5G, 6G, and beyond. Despite their advantages, UAV-based networks also introduce unique challenges, particularly in data transmission management. Unlike traditional terrestrial networks, UAVs operate in three-dimensional space, significantly increasing mobility complexity [14; 15]. Free-space orientation and antenna sidelobes impose constraints on transmission, making data transfer in UAV-based networks a critical issue in both 5G and 6G environments.