People with high-level cervical spinal cord injuries can experience significant impairments in their ability to control their environment, including challenges in operating a smartphone or navigating a power wheelchair. The use of eye-tracking technology has been crucial in improving communication and control for individuals with tetraplegia. However, traditional eye-tracking systems often have limitations in terms of accuracy, calibration time, and practicality. To overcome these limitations, researchers have explored the use of Convolutional Neural Networks (CNNs) in AI-enhanced eye-tracking technology. CNNs are a type of deep learning algorithm that can learn complex patterns in image data, allowing for more accurate and reliable eye tracking. AI-enhanced eye tracking that utilizes triple blinking is a novel approach showing great potential for improving the accuracy and efficiency of eye tracking technology. By employing advanced machine learning algorithms, this method can detect and track eye movements based on the number of blinks, providing a more reliable and efficient way to interact with digital devices. This technology has the potential to revolutionize the way people engage with digital devices, making them more accessible and user-friendly for individuals with disabilities or impairments. The findings related to AI-enhanced eye tracking using triple blinking suggest that it can be a viable alternative to traditional eye tracking technology, which can be costly, time-consuming, and difficult to use. Furthermore, this approach is highly customizable and can be adapted to meet the specific needs and preferences of individual users. As such, it has the potential to significantly enhance the quality of life for individuals with motor impairments, visual impairments, or other disabilities that affect their ability to use traditional eye tracking technology. AI-enhanced eye tracking using triple blinking is a promising innovation that could contribute to a more inclusive and accessible digital world. With continued research and development, even more innovative solutions and applications for this technology are expected in the future.