The weathering process of grottoes is directly influenced by environmental parameters. Consequently, estimating these parameters is important for the effective preservation of Yungang Grottoes. This research utilized measured time-series data of wall temperature, environmental humidity, and temperature from the 10th grotto of Yungang Grottoes. These data were decomposed into various components using empirical mode decomposition (EMD). Correlations between the measured time-series data and intrinsic mode function (IMF) components were also investigated. A combined model, based on the EMD-long short-term memory (LSTM) artificial neural network (ANN), was then developed. Using mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R² as the evaluation indices, comparisons were made be- tween the recorded environmental parameters and those estimated by the combined model and standalone LSTM-based ANN. The findings suggested that as the rate of change in the IMF components increased, the correlation between the IMF components and measured time-series increased. When only employing the LSTM-based ANN, optimal results were obtained with 2 hidden layers and an initial learning rate of 0.001. Conversely, when using the combined model, MAE, RMSE, and MAPE values decreased, while R² values in- creased, indicating the improved estimation efficiency. The accuracy of the environmental parameter estimations largely depended on the extent of parameter changes, with smaller changes leading to better model efficiency. The insights gained from this research can be useful for the preservation of cultural relics of grottoes.