Satsafe-Ai: Ai-Based Satellite Collision Prediction and Anomaly Detection System

The increasing population of operational satellites, inactive spacecraft, and orbital debris has intensified the need for continuous space-object monitoring and collision-risk assessment. This paper presents SATSAFE-AI, a real-data-based satellite safety monitoring system that integrates orbital data collection, orbit propagation, close-approach analysis, statistical anomaly detection, visualization, and simulated collision avoidance decision support. The system retrieves Two-line Element (TLE) records from CelesTrak and applies the Simplified General Perturbations 4 (SGP4) model to estimate satellite and debris positions and velocities at selected times. Relative position and separation-distance analysis is used to identify close approaches, while a ZScore-based method highlights observations that differ significantly from the measured orbital-data distribution. A web application developed using Python and Flask provides data management and monitoring functions, with MySQL used for persistence and CesiumJS and Plotly supporting orbital and analytical visualization. The system presents risk information and simulated orbit-adjustment recommendations for user examination; it does not command real spacecraft or execute manoeuvres. SATSAFE-AI therefore provides an integrated prototype for understanding orbital movement, investigating unusual behaviour, and supporting preliminary satellite-safety analysis. The paper describes the system design and methodology; quantitative performance claims are not made without validated experimental measurements.