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Real-time stream processing for anomaly detection in network traffic is an advanced system designed to monitor, analyze, and identify unusual patterns in network data as it flows continuously. With the rapid growth of internet usage and cyber threats, traditional batch processing methods are no longer sufficient to detect intrusions or anomalies in a timely manner. This project focuses on building a real-time system that captures streaming data packets from network sources, processes them instantly, and identifies anomalies using statistical and machine learning techniques. The system leverages technologies such as data streaming frameworks, real-time analytics engines, and anomaly detection algorithms to provide immediate insights. It processes high-volume network traffic, extracts relevant features such as IP address, protocol type, packet size, and time intervals, and applies models to detect deviations from normal behavior. The objective is to reduce response time to cyber threats and improve network security. This project is highly useful for organizations that require continuous monitoring of network traffic to prevent attacks such as Distributed Denial of Service (DDoS), intrusion attempts, and data breaches