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Beyond 5th generation (B5G) mobile communication framework is a paramount research domain in modern networking and message transfer. In 6G communication, the prime objective is ultralow latency communication, which ensures high bandwidth data transfer in real-life and mission-critical situations. In a 6G networking scenario analysis and prediction of network throughput, from the location, trajectory, moving speed, heading of the user equipment, drone base stations, receiver, and relay node informations are highly important for achieving efficiency in the resource utilization. In this work, we analysed the network latency using simulation framework. Furthermore, we propose a neural network model that can intelligently predict network throughput parameters of a 6G communication ecosystem. The system uses an improvisation regarding the prediction optimization technique, coined “3-Musketeer Optimization”. The prediction was performed by using an ensemble artificial neural network model on the throughput with the parameters, mobility, trajectory, signal strength, power, radio status of user equipment and moving base stations. The results show that the latency of the node is a maximum of 800 ms and a minimum of 170 ms, and the mean square error (MSE) and mean absolute error (MAE) of the 3-Musk optimizer are approximately 0.0275 and 0.1125, respectively.