## 6 ML-Driven RIS-NTN Systems: Future Perspectives and Challenges  
The RIS is a groundbreaking technology in wireless communication networks, potentially significantly changing how data are transmitted, received, and handled [132]. Through the intelligent reflection and manipulation of EM waves, RISs can improve signal strength, reduce interference, and enhance network performance across various settings [120; 133]. However, conventional optimization strategies relying on CSI encounter intrinsic constraints that limit their effectiveness in fully utilizing the potential of RIS technology. These limitations arise from wireless channels' intricate and fluctuating nature and the computational burden linked to real-time adjustments and optimization. Therefore, a critical requirement is for more effective and adaptable optimization approaches to leverage the complete capabilities of RIS-enhanced networks [134; 15; 135]. Recently, ML has become a promising method for overcoming the optimization obstacles that RIS technology encounters. Through utilizing data-driven observations and adaptable algorithms, ML methods can enhance RIS performance in real-time by constantly adjusting phase shifts and beamforming patterns to maximize signal strength and reduce interference [136; 137]. Advancements in optimization driven by ML have shown considerable enhancements in RIS performance in various areas, such as mapping, propagation modeling, localization [138], signal processing, and resource allocation. ML algorithms can dynamically optimize RIS setups to accommodate evolving environmental conditions and user needs by analyzing large datasets and identifying intricate wireless channel behavior [126; 135]. Nevertheless, while ML-driven optimization holds potential, various challenges and research hurdles must be tackled. One notable difficulty involves the requirement for extensive datasets to train ML models. Moreover, real-time optimization presents significant computational complexity, and ensuring the resilience of ML algorithms in dynamic and uncertain wireless environments is also a significant obstacle [15; 126]. Furthermore, integrating ML with RIS technology necessitates addressing interoperability issues and guaranteeing smooth integration with current network architectures. Looking to the future, the combination of ML and RIS technology has the potential to transform wireless communication networks, leading to more effective, adaptable, and intelligent systems. By tackling the issues associated with conventional optimization methods and leveraging data-driven insights, ML-driven RIS optimization offers groundbreaking improvements in network performance, dependability, and scalability. The convergence of ML and RIS marks a significant milestone in advancing wireless communication networks. Through the utilization of adaptive optimization and intelligent reflection principles, this ML-driven RIS technology offers an opportunity to unlock the complete capabilities of future wireless networks, thereby influencing connectivity in the digital era [139; 16; 134]. Several future challenges for implementing RISs in NTNs using ML techniques are presented as follows:  
**Resource-efficient RL Methods:** Despite promising results, one significant challenge is developing accurate RL methods with moderate computational and energy demands suitable for aerial platforms. The focus should be on ensuring that RL techniques comply with the resources available to the aerial platforms, especially considering real-world scenarios' dynamic and stochastic nature. **Integration with Advanced Technologies:** The evolution of NTNs to support super-high-definition (SHD) and extremely high-definition (EHD) videos with super-high throughput demands and ultra-reliable low-latency communications presents a complex challenge. Integrating RISs into this landscape becomes a multifaceted task.