This paper details the design, implementation, and performance evaluation of a low-cost, real-time Laser-Guided Face Tracking System. The system successfully integrates computer vision principles with mechatronic actuation to maintain continuous surveillance on a detected human face. The core architecture is a split-control model: a host computer executes a Python application utilizing the OpenCV Haar Cascade classifier for live face detection and coordinate extraction from a camera feed. These two-dimensional pixel coordinates are dynamically mapped to angular commands for the actuator. Communication is handled via a serial protocol that transmits the mapped angles to an Arduino Uno microcontroller. The Arduino, functioning as the embedded control unit, generates the necessary Pulse Width Modulation (PWM) signals to drive two orthogonally mounted servo motors in a pan-tilt configuration. A low-power laser diode, fixed atop the assembly, visually confirms the tracking lock. The system achieves a mean steady-state tracking error of less than 5 pixels and a latency below 150 milliseconds, demonstrating a viable, accessible solution for applications requiring real-time directional control. This work validates the synergistic approach of combining high-level vision processing with simple, effective embedded hardware.