Abstract
The fast change from 5G to 6G networks calls for extremely accurate network traffic prediction and effective resource allocation to meet rising data volumes and ultra-low latency requirements. To deal with the complicated time and space based aspects of 6G network traffic, an AI based hybrid model is developed that combines random forest (RF), gated recurrent units (GRU), and a mechanism for paying attention is proposed. Large-scale 6G traffic data with varied channel conditions and user scenarios was used to validate the model. An algorithm is presented to describe the training process of the proposed hybrid model. The results of the proposed hybrid model are presented and compared with baseline methods, including LSTM, GRU, random forest, and XGBoost. Our model obtains a Root Mean Squared Error (RMSE) of 0.0049, an Mean Absolute Error (MAE) of 0.0034, a mean absolute percentage error (MAPE) of 0.46%, and a coefficient of determination R 2 of 0.9970 according to experimental findings on a whole dataset. The suggested technique lowers the RMSE by over 69% and increases R 2 by up to 2.88% compared to baseline GRU and LSTM respectively. These results highlight how well combining deep sequence modelling with ensemble learning works. In next-generation wireless systems, the framework opens the path for proactive resource allocation, strong security, and real-time optimisation outside of improving forecast accuracy. Moreover, this paper provides a critical review of open research directions including the scalability of hybrid AI models, edge intelligence integration, and the evolution of standardised protocols for safe and smooth AI deployment in 6G networks.