This paper describes a deep learning method such as the long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), gated recurrent unit (GRU), bidirectional gated recurrent unit (Bi-GRU) to design for forecasting in terms of solving this problem. The data for the study was obtained on the Eurostat website from January 2017 to June 2022, and the data for monthly gas consumption used the technique of walk-forward validation (WFV) to evaluate the generalization ability prediction. Based on error scores, the study results compared the accuracy rates of the four models’ approaches show that: the Bi-GRU model was the best prediction for Bulgaria, Hungary, and Romania, the LSTM model best approached Slovakia, and the GRU model was the best suitable for Poland and Czech. The predicted methods for effectively managing the natural gas resource are crucial for enhancing gas consumption’s efficiency and reducing its impact on the environment. In addition, the result may contribute to stakeholders taking the right decision for energy planning.