This thesis delves into the development of an interactive robotic eye system that integrates both real-time face recognition and voice-based interaction. It is built using a Raspberry Pi, PCA9685 servo driver, and 3D-printed eye structure, the system aims to deliver dynamic, human-like eye movements with detected faces using the OpenCV, MediaPipe and CVZone libraries. Additionally, a voice assistant powered by speech-to-text and text-to-speech libraries to communicate with users and assist them with features like task reminders, alarms, and object location memory. These tools are used to create a reliable face tracker that includes a responsive assistant side of the project. By merging vision and speech-based interaction, the system offers a step towards social communication and a better human-machine interface. The results demonstrate reliable face tracking and responsive voice assistant functions which demonstrate the potential for utilizing it as a personal companion.