Trackify -GPS and ML Based Vehicle Monitoring

With the increasing demand for intelligent transportation systems, real-time vehicle tracking and risk analysis have become essential for ensuring safety, operational transparency, and efficient fleet management. This paper presents Trackify, a GPS and Machine Learning (ML)-based vehicle monitoring system designed to provide proactive tracking, anomaly detection, and launch risk estimation. The system integrates a Neo-6M GPS module with an ESP32 microcontroller to capture real-time geographic coordinates and transmit them to a Firebase Realtime Database using Wi-Fi communication. The cloud-based data is processed through clustering, anomaly detection, and trend analysis models to evaluate vehicle behavior and traffic density patterns. A rule-based risk estimation module combines outputs from clustering and anomaly detection models to generate a comprehensive risk classification for different operational zones. The entire system is deployed through a web-based dashboard developed using modern web technologies, enabling visualization of live vehicle positions, anomaly insights, and risk levels. The proposed system demonstrates low-latency synchronization, scalable cloud integration, and effective monitoring capabilities, offering a cost-effective alternative to traditional GSM-based tracking systems. . Key terms- GPS Tracking, Machine Learning, IoT, Firebase, Geofencing, Anomaly Detection, Vehicle Monitoring.