## 1 Introduction  
Non\(-\)terrestrial network (NTN) communication is pivotal for overcoming the coverage limitations of terrestrial networks (TNs), ensuring reliable and seamless connectivity under challenging conditions and enabling global communication in sixth\(-\)generation (6G) wireless systems [1, 2]. Low Earth orbit (LEO) satellites, with their extensive coverage, reduced latency, and cost\(-\)effective deployment [3], have emerged as a cornerstone for addressing the constraints of traditional infrastructure, facilitating efficient global connectivity [4]. However, the rapid movement of LEO satellites results in limited coverage time for terrestrial terminal groups (TTGs), presenting challenges in providing continuous service. Thus, designing effective handover policies is crucial to maintain high\(-\)quality service in dynamic satellite networks. In recent years, numerous studies have investigated handover strategies. In [5], the authors proposed a strategy based on the distance between satellites and TTGs, selecting the nearest satellite for handover. By comprehensively evaluating the signal\(-\)to\(-\)interference\(-\)plus\(-\)noise ratio (SINR), bandwidth utilization, transmission delay, packet loss rate, and monetary cost, the authors of [6] proposed a service\(-\)adaptive multi\(-\)criteria vertical handover algorithm that reduces the dropping probability and enhances the over all system throughput. Furthermore, the authors of [7] proposed a multi\(-\)metric handover method that uses the Floyd algorithm to identify the shortest path in a dynamic graph, effectively reducing the total handover delay and avoiding unnecessary handovers. Nevertheless, these methods primarily considered network conditions at the moment of decision\(-\)making. As an emerging deep learning technique, deep reinforcement learning (DRL) facilitates the maximization of cumulative rewards through repeated trial\(-\)and\(-\)error interactions, making it especially suitable for long\(-\)term optimization problems. In [8], the authors introduced a satellite handover strategy based on multi\(-\)agent Q\(-\)learning, aiming to minimize the average satellite handovers while satisfying the load constraints of each satellite. In [9], a centralized\(-\)training\(-\)and\(-\)distributed\(-\)execution scheme based on deep Q\(-\)networks (DQNs) was proposed, where environmental parameters were trained by a training node in the backhaul network and are distributed to TTGs for independent handover decisions. Additionally, the authors of [10] addressed the freshness of information and proposed an age\(-\)oriented satellite handover strategy based on dueling\(-\)double\(-\)deep\(-\)Q\(-\)learning (D3 QN) to minimize the long\(-\)term peak age of information. However, these approaches failed to accurately model the communication traffic demands of TTGs, which could result in a large number of TTGs simultaneously accessing a previously underloaded satellite, causing network load imbalances and degrading quality of service (QoS). Traffic prediction utilizes historical traffic data and advanced algorithms to forecast communication demands, establishing a robust foundation for subsequent decision \(-\)making [11]. Early studies relied on simple learning methods, such as linear regression [12] and support vector regression (SVR) [13], for traffic prediction. With the advent of neural networks, their superior generalization ability and predictive accuracy have made deep learning the dominant choice for modern traffic prediction methods. The authors of [14] addressed wireless traffic forecasting with an attention\(-\)enhanced deep residual convolutional neural network (CNN). On the other hand, the authors of [15] reformulated traffic prediction as a classification task, using CNNs to assign traffic in fixed time intervals to predefined categories, and thus, improve the accuracy. As a step further, to capture temporal dependencies, the authors of [16] employed gated recurrent units (GRUs) to achieve precise traffic forecasts.