The entry condition of the Severe Accident Management Guideline (SAMG) in Nuclear Power Plants (NPPs) is determined by the Core Exit Temperature (CET). If the CET exceeds 922 $K$ (1200 ${}^{\circ}F$), severe accident management begins. Because a severe accident can induce a large scale of damage, it is necessary to prepare for such accident and take preemptive actions. However, the operators may be confused by the complexity of the system, which can delay actions. Therefore, operators need the entry time information of SAMG to act proactively. In this study, the entry time was predicted through CET prediction. The Explainable Boosting Machine (EBM) model was used to select the input variables and the Long Short-Term Memory (LSTM) model was used to predict CET 600 seconds ahead. And the Monte Carlo (MC) dropout method was used to evaluate the uncertainty of the predictions at a 95% confidence level. As a result, the LSTM model performed well and the evaluated uncertainty provided confidence in the predictions with a confidence interval. Predicting 600 seconds ahead provides time for the operators to take actions on the accident, and the uncertainty evaluation adds reliability to the model's prediction. The results of this study are expected to be used as part of the operator support system and serve as a means for rapid accident mitigation actions. Furthermore, the integration of AI-based predictive models and uncertainty evaluations ensures that operators are equipped with reliable information, enhancing their ability to act preemptively and effectively in response to severe accidents.