Speaker region identification is an essential task in many speech processing applications such as speaker recognition, speaker diarization, and automatic speech recognition. This research paper proposes a Recurrent Neural Network (RNN) based model for speaker region identification. The model is trained on a dataset of speech recordings from multiple regions and is tested on a separate evaluation set. Experimental results show that the proposed model outperforms state-of-the-art methods in terms of accuracy and robustness.