Maintaining data integrity is crucial in network monitoring and management, as it directly influences decision-making. However, various challenges such as network disruptions, hardware failures, and human errors often result in incomplete or corrupted data. These issues can significantly impact the effectiveness of security systems and analytical tools. This paper introduces an advanced data recovery and anomaly detection framework called the Data Recovery Network Monitoring System (DRNMS). The system is designed to restore lost or damaged data while simultaneously identifying unusual patterns in network traffic. It leverages improved Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models to enhance detection accuracy and reliability. Additionally, DRNMS employs an Autoencoder for continuous network monitoring, ensuring real-time anomaly detection. The models were trained using network traffic datasets, and experimental evaluations confirm the systems effectiveness in preserving data integrity and detecting potential security threats. Furthermore, a structured recovery algorithm is proposed to systematically reconstruct missing or corrupted data. Performance assessments based on accuracy, precision, and recall highlight DRNMS as a highly efficient solution for industries that demand reliable network monitoring and security.