The rapid advancement of communication and information systems has led to a surge in Internet data generation, resulting in increased cyber attacks and intrusion techniques. To counter this, sophisticated network intrusion detection systems are crucial for safeguarding computer networks against diverse cyber threats. Traditional rule-based or signature-based methods fall short against new attacks, necessitating more advanced approaches. The proposed methodology involves analyzing different RNN models like simple RNN, LSTMs, etc. to create a NIDS capable of accurately identifying intrusion attacks within network traffic. This model is then compared against pre-existing neural networks, artificial neural networks, and machine learning algorithms to enhance its performance. The Network Intrusion Detection System model, powered by deep learning neural networks, seeks to provide an effective solution for detecting various dangerous network threats like DOS, R2L, U2R, and probing.