This paper proposes a hybrid artificial intelligence (AI) model that combines an Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) to predict the Quality of Service (QoS) in 5G networks. The model utilizes radio channel indicators such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), Received Signal Strength Indicator (RSSI), and Channel Quality Indicator (CQI) to forecast throughput and latency levels. These indicators are critical factors affecting network performance; however, the nonlinear relationship among them makes traditional analytical models inadequate for accurate QoS prediction. The importance of the current study lies in estimating QoS in 5G networks by combining radio-level physical-layer indicators into a neural network based on the PSO algorithm. The proposed approach allows for better modeling of QoS dynamics and improvement in prediction accuracy. The hybrid ANN-PSO model that integrates an Artificial Neural Network (ANN) with a Particle Swarm Optimization (PSO) algorithm is compared with traditional methods such as Linear Regression and Random Forest to evaluate prediction accuracy. Experimental results demonstrate that the proposed model achieves higher accuracy and lower prediction error, making it a promising tool for predictive QoS optimization in next-generation 5G systems.
