Panel rockfill dams, due to their cost-effectiveness, short construction periods, high safety standards, and strong adaptability to foundations, have gained rapid development and considerable attention in recent years. However, the inherent material properties and structural behavior of panel rockfill dams change continuously during operation. Particularly, long-term operation results in coordination issues between the panel concrete and the rockfill material, leading to deformations. Regular monitoring and prediction of horizontal displacements in panel rockfill dams are crucial to ensure the dam's safety. This study focuses on the robustness of two prediction regression models, namely, displacement statistical regression models and grey prediction monitoring models, applied to a panel rockfill dam in the Lower Elesai Hydropower Project in Cambodia. The research explores the accuracy, applicability, strengths, and weaknesses of these two commonly used regression models for fitting and predicting deformations, such as displacements, in panel rockfill dams. Furthermore, both prediction regression models are employed to analyze horizontal displacement monitoring data of the panel dam, conducting an in-depth study of deformation patterns. This analysis aims to determine the initial reservoir period's coordination patterns of deformation and provide recommendations based on the findings.