## 7 Conclusions  
Technology is developing with unpredictable momentum. The demands for this development are both increasing and deepening. Future communication technologies demand Tbps-level speeds, end-to-end latency rates under milliseconds, a connection density of \(10^{7}\) per square kilometer, and mobility of up to 1000 km per hour, with access to all these features from any location at any time. To fulfill these requirements, the availability and coverage of satellite networks is a key point. There is no suspicion that both 6G networks and satellite networks will progress arm-in-arm at a point of transformation in communication. However, routing protocols in these networks are also one of the important processes that affect throughput and demands. At this point, new routing approaches with high flexibility, invulnerability, and the ability to adapt to variable conditions are needed. This paper presents a comprehensive analysis of 6G, satellite networks, and routing techniques based on these networks. A detailed literature review of 6G and satellite networks, algorithms, key performance indicators, key enablers, challenges (and their control), and a detailed review of future directions was carried out. To begin with, this paper presents the history of network generations from 1G to 6G. Specifically, the paper focuses on satellite and 6G networks and routing techniques and management. Then, the classification of routing techniques and the limits of these routing approaches are explained in detail. The methods of routing algorithms in 6G networks have been investigated, and the studies carried out have been presented. Satellite networks, routing protocols in satellite networks, and proposed routing algorithms for LEO networks have been examined from a broad perspective. This paper presents the challenges in routing management caused by the fact that the dynamic environments of 6G and \begin{table}
\begin{tabular}{c l l l l} \hline \hline
**Year** & **Article** & \begin{tabular}{l} **Major Features and** \\ **Characteristics** \\ \end{tabular} & **Network Type** & **Key Contributions** & **Challenges Addressed** \\ \hline \multirow{3}{*}{2018} & [143] & Real-time deep & \multirow{3}{*}{6G} & Dynamic traffic routing & \begin{tabular}{l} Scalability in \\ high-density networks; \\ real-time \\ processing demands. \\ \end{tabular} \\  & & learning-based traffic & \multirow{3}{*}{6G} & \begin{tabular}{l} Dynamic traffic routing \\ with AI-driven \\ adaptability. \\ \end{tabular} & \begin{tabular}{l} Scalability in \\ high-density networks; \\ real-time \\ processing demands. \\ \end{tabular} \\  & & deep CNNs. & & \begin{tabular}{l} Identifies gaps in \\ interoperability and \\ latency for \\ AI/ML-driven routing. \\ \end{tabular} \\  & & & & & & \\ \hline \multirow{3}{*}{2020} & [145] & \begin{tabular}{l} Proposes RIRM (Reliable \\ Intelligent Routing \\ Mechanism) for 5 GC \\ congestion management. \\ \end{tabular} & 6G & \begin{tabular}{l} AI-based load-balancing \\ for ultra-reliable \\ low-latency \\ communication (URLLC). \\ \end{tabular} & \begin{tabular}{l} Congestion in \\ high-density scenarios; \\ reliability under mobility. \\ \end{tabular} \\  & & & & & \\ \hline \multirow{3}{*}{2021} & [122] & \begin{tabular}{l} Reviews satellite/HAPs \\ network layers; compares \\ RF and FSO solutions. \\ \end{tabular} & Satellite & \begin{tabular}{l} Highlights trade-offs \\ between RF (robustness) \\ and FSO \\ (high bandwidth). \\ \end{tabular} & \begin{tabular}{l} Signal attenuation; \\ atmospheric interference \\ in FSO. \\ \end{tabular} \\  & & & & \\ \hline \multirow{3}{*}{2022} & [146] & \begin{tabular}{l} Integrates Distributed AI \\ with neural networks for \\ energy-efficient routing \\ in ITS. \\ \end{tabular} & 6G/Satellite & \
\begin{tabular}{l} Optimizes intra-cluster \\ communication for rapid \\ node mobility. \\ \end{tabular} &
\begin{tabular}{l} Energy efficiency; \\ handling \\ dynamic topologies. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}