India has experienced significant growth in cyberattacks in recent years, with nearly 1.39 million cyber incidents reported in 2022 alone, according to CERT-In (Indian Computer Emergency Response Team). The adoption of digital transformation and IoT devices has made edge computing critical for decentralized data processing. However, this distributed nature of edge devices makes them vulnerable to cyber threats, especially in real-time environments. Edge computing enhances data processing at the source, enabling low-latency, real-time applications like autonomous vehicles and smart cities. Ensuring the security of these decentralized systems is crucial for maintaining trust and performance.To develop an AI-driven intrusion detection system leveraging advanced feature selection techniques and supervised learning models for real-time threat detection in edge computing environments. Before machine learning, traditional intrusion detection relied on rule-based systems, firewalls, and signature-based antivirus software. These systems were often static, required manual updates, and lacked the ability to detect novel or zero-day attacks. Traditional systems are reactive, inefficient, and incapable of handling large-scale, dynamic threats. Their inability to process complex, real-time data at the edge leads to delayed detection and increased vulnerability to sophisticated cyberattacks. The growing frequency and sophistication of cyberattacks in edge computing demand innovative solutions. Traditional systems fail to adapt to evolving threats, creating a gap that AI and machine learning can fill. The integration of real-time analytics and feature selection addresses these challenges effectively. The proposed system uses AI-based models with advanced feature selection techniques to analyze edge data in real time, identifying anomalies and intrusions. Supervised learning algorithms like Random Forest, and neural networks improve detection accuracy by learning from labeled datasets. This approach enhances the ability to detect zero-day attacks, reduces false positives, and ensures efficient resource utilization, strengthening overall security in edge computing.