###### Abstract
This continuous motion leads to frequent handover events; for instance, a satellite orbiting at 600 km altitude with a beam diameter of 50 km may necessitate a handover approximately every 5 s. Such frequent handovers exacerbate signaling overhead, and when combined with measurement inaccuracies and negligible signal strength differences between overlapping cells, the risk of service failures and extended interruptions increases significantly. As shown in [18], the UE-assisted network-controlled baseline handover (BHO) struggles to maintain robust service quality due to delayed handover decisions. Meanwhile, findings in [19] highlight that the Rel-16 conditional handover (CHO) reduces radio link failures (RLFs) and handover failures through earlier handover preparations. However, this comes at the expense of a 60% increase in signaling overhead and measurement reporting compared to the BHO. Both approaches rely solely on UE-provided measurements, which directly affect the timing and accuracy of handover triggers and decisions. While these measurement-based handover methods are effective and widely adopted in terrestrial networks due to their simplicity, they fall short of meeting the unique demands of NTN's dynamic environment. Nevertheless, for NTN implementations, the 3 GPP has recommended in reference [17] the incorporation of additional triggering criteria, as a purely \(RSRP\)-based handover (RBH) triggering--where \(RSRP\) is defined as the received power of reference signals--may not suffice to achieve suitable mobility performance. The successful implementation of ML solutions for mobile network automation has driven its automated applications in 5G technology, as it provides a solution for achieving the desired adaptability with low management costs. The authors in [20] applied a method called \(\varepsilon\)-greedy Q-learning aimed at learning the suitable handover strategy that maximizes the expected future throughput based on pedestrian location and speed. The authors in [21] proposed a method to improve conditional HO techniques by preparing the target cell for potential upcoming handover through predictions. Their results indicate promising improvements in reducing the Mitigation Time (MIT). The work in [22] focuses on using Long Short-Term Memory (LSTM) supervised machine learning techniques for multi-user, multi-step trajectory prediction. In [23], the authors proposed a method to enhance handover performance in millimeter-wave vehicular networks by leveraging historical handover data combined with basic machine learning algorithms. This approach aimed to identify patterns and correlations between vehicle status parameters at the time of the handover request and the resulting handover decision. While effective for the targeted vehicular network scenarios, their solution is highly scenario-specific and lacks the adaptability required for more complex and diverse contexts, such as broader 5G, 6G, or NTN environments. The integration of NTNs into 6G enables global connectivity but introduces challenges such as latency, mobility, and resource allocation. To address these, ref. [24] reviews AI techniques, including ML, DL, and DRL, for optimizing NTN operations like network planning and interference management while also discussing security concerns. Similarly, ref. [25] highlights AI's role in mitigating satellite NTN challenges such as Doppler effects, handovers, and spectrum sharing, emphasizing AI-driven task offloading and network slicing. Both studies underscore AI's potential to enhance NTN efficiency while acknowledging ongoing research challenges. In [26], [PERSON] et al. proposed a deep reinforcement learning-based satellite handover scheme to optimize handover decisions in satellite communications. Their work demonstrated the potential of DRL in improving handover efficiency by learning from past mobility patterns. However, their approach did not refine the obtained location information, which could lead to inaccuracies in handover decisions.