Ensuring healthy vision in infants requires early detection of potential problems. Current Infant Visual Function Measurement Systems built in previous GSOC 22 and GSOC 23 are efficient and robust solutions but are limited by difficulties with infant cooperation and shortcomings in eye-tracking technology. To address this, a comprehensive upgrade is proposed. A user-friendly and visually engaging interface will be built for researchers and clinicians. Additionally, a wider variety of captivating stimuli, both visual and auditory, will be incorporated to keep infants engaged during testing. The core functionality, eye-tracking, will be significantly improved by exploring advanced deep learning techniques. This will enable precise gaze estimation in all directions, including up and down, providing a more complete picture of an infant's visual attention. Furthermore, a novel test will be developed to identify potential vision issues associated with anisometropic amblyopia. Finally, the system will be designed for seamless integration with various external eye-tracking hardware devices, increasing its flexibility and applicability. This project strives to create a user-friendly and robust Infant Visual Function Measurement System, empowering researchers and clinicians with a powerful tool for early detection and intervention, ultimately leading to better visual development for young children.