The investigation of ion cluster center-of-mass (COM) motion trajectories is of paramount importance in comprehending and regulating the behavior of ion clusters. The analysis of data reveals that the COM motion trajectories of ion clusters are characterized by a high degree of chaoticity. In accordance with the principles of chaos theory, while chaotic sequences exhibit unpredictability on macroscopic or large temporal scales, they possess definite predictability on microscopic or small temporal scales. Considering this, this study proposes a prediction model for the ion cluster COM motion trajectories based on a multiple Echo State Network (ESN) framework. In this framework, each ESN is responsible for the prediction of ion cluster COM motion trajectories in a local space or a short time. A distinctive attribute of ESNs is their simplicity in computation, which distinguishes them from other neural networks. However, it should be noted that the application of ESNs to the prediction of ion cluster COM motion trajectories has not been directly reported in the existing literature. The experiments presented in this study shows that the proposed prediction model achieves superior results in the prediction of ion cluster COM motion trajectories at 5000 time points, with prediction errors of 0.0202, 0.0208 and 0.0196 in the x, y, and z directions, respectively, and an overall error of 0.0202. These values are substantially lower than the prediction errors of the model that utilizes a single ESN over the same time points, which is 0.12.