This study presents the design and implementation of an automated vehicle control system aimed at improving safety and operational efficiency at the Instituto Superior Tecnológico del Azuay. The solution integrates computer vision (OpenCV) and optical character recognition (PyTesseract) for license plate reading, an Arduino-based physical module for the entry bar operation, and a MySQL database connected to a Flask web interface for real-time monitoring and visitor registration. The collected data is integrated into dynamic Power BI dashboards, displaying metrics such as authorized access, peak hours, occupancy, and unauthorized access. The development followed the CRISP-DM methodology and was validated in two stages: first on a scale model and then in tests with real vehicles within the institution. The system achieved an accuracy of 91% under optimal conditions and 75% under adverse scenarios, with response times between 2 and 3 s. The prototype reduces manual intervention, strengthens access control, and establishes a scalable foundation for future deployments in institutional environments.