Abstract: Groundwater is the sole natural freshwater supply on coral islands. Accurately predicting its level dynamics is of great significance for island water resource management and ecological protection. Currently, long-term in situ monitoring data for coral island groundwater levels remain relatively limited, hydrogeological parameters across different islands are difficult to obtain, and dynamic prediction based on high-frequency monitoring data is particularly lacking. Based on continuous groundwater observation data from a typical coral island in the South China Sea between 1 November 2021 and 10 August 2024, this study analyzed the dynamic patterns of groundwater levels across different time scales. Subsequently, a hybrid prediction model combining wavelet analysis with a long short-term memory network (wavelet-LSTM model) was constructed to achieve the prediction of groundwater levels in coral islands. The results indicate that the groundwater level ranged from −0.5 to 2.5 m, with an average level of 0.90 m. It presented significant multi-time-scale fluctuation characteristics, primarily including seasonal cycles, monthly cycles, daily cycles, and high-frequency pulse-like variations. Among these, seasonal and high-frequency variations were mainly controlled by precipitation, while monthly and daily cyclical changes were closely related to tidal dynamics. Model validation demonstrates that the proposed wavelet-LSTM model achieved high predictive accuracy and showed excellent robustness. Moreover, the model showed strong transferability to independent coral-island settings with similar hydrogeological characteristics, highlighting its potential for regional application. The wavelet-LSTM model constructed in this study can effectively characterize the multi-time-scale dynamic features of coral island groundwater systems. Its demonstrated generalization capability offers a practical and efficient approach for groundwater assessment and sustainable water-resource management on coral islands where hydrogeological observations are limited.