In our previous study (Moon et al., 2020), we developed a Long Short Term Memory (LSTM) deep-learning model for geomagnetic quiet days to perform effective long-term predictions for the regional ionosphere. However, their model could not predict geomagnetic storm days effectively at all. This study developed an LSTM model suitable for geomagnetic storms using the new training data set and re-designing input parameters and hyper-parameters. We collected 131 days of geomagnetic storm cases from 1 January 2009 to 31 December 2019, and obtained the IMF Bz, Dst, Kp, and AE indices related to the geomagnetic storm corresponding to each storm date. These indices and F2 parameters of Jeju ionosonde (33.43˚N, 126.30˚E) were used as input parameters for the LSTM model. To test and verify the predictive performance and the usability of the LSTM model for geomagnetic storms developed in this manner, we created and diagnosed the 0.5, 1, 2, 3, 6, 12, and 24-hour predictive LSTM models. According to the results of this study, the LSTM storm model for 24-hour developed in this study achieved a predictive performance during the geomagnetic storms about 32% (10%), 34% (17%), and 37% (5%) better in RMSE of foF2 (hmF2) than the LSTM quiet model (Moon et al., 2020), SAMI2, and IRI-2016 models. We propose that the short-term predictions of less than 3 hours are sufficiently competitive compared to other traditional ionospheric models. Thus, this study suggests that our model can be used for short-term prediction and monitoring of the regional mid-latitude ionosphere.