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In today’s dynamic digital ecosystem, cybersecurity remains a critical challenge due to the growing sophistication of network threats and the limitations of traditional intrusion detection systems (IDS). Existing systems often rely on real-world network traffic data, which is scarce, unlabeled, or restricted by privacy regulations, making the development of robust detection models difficult. To address these challenges, this project introduces an AI-driven threat detection framework built on synthetic network traffic analysis. Synthetic traffic is generated through Python-based scripts, enabling scalable and diverse datasets that replicate realistic benign and malicious network behaviors without compromising sensitive data. The proposed system integrates preprocessing, feature extraction, and machine learning model training to classify network traffic as normal or malicious. Model performance is assessed using metrics such as accuracy, precision, recall, and confusion matrix analysis to ensure reliability. By utilizing synthetic traffic, the approach effectively bypasses issues of data availability and privacy, while offering a scalable, adaptive, and regulation-compliant solution. This research not only establishes the feasibility of synthetic data in enhancing intrusion detection but also provides a proof-of-concept that can be extended to real-world IDS and SIEM deployments for intelligent, adaptive cybersecurity.