Abstract: The increasing demand for intelligent defense systems has driven the development of advanced threat prediction models. This paper presents SHIELD-A, a strategic AI-based air defense simulation system designed to analyze and predict aerial threats in real time. The proposed system utilizes machine learning techniques to improve prediction accuracy while reducing computational complexity compared to traditional methods such as inverse reinforcement learning. By employing simulated datasets, the system achieves faster response times and enhanced efficiency. Additionally, geospatial visualization and real-time alert mechanisms improve situational awareness and user interaction. The proposed approach is suitable for applications in defense, surveillance, and security systems.

Keywords: Artificial Intelligence, Machine Learning, Air Defense, Threat Prediction, Simulation System

Downloads: | DOI: 10.17148/IARJSET.2026.13444