Unmanned aerial vehicles (UAVs), commonly known as drones, have revolutionized numerous fields ranging from aerial photography to surveillance and logistics. Achieving stable flight is essential for their successful operation, ensuring accurate data acquisition, reliable manoeuvring, and safe operation. This thesis explores the feasibility of employing a frontal mono camera and sensor fusion techniques to enhance drone stability during flight. The objective of this research is to investigate whether a frontal mono camera, combined with sensor fusion algorithms, can be used to effectively stabilize a drone in various flight scenarios. By leveraging machine vision techniques and integrating data from onboard gyroscopes, the proposed approach aims to provide real-time feedback for controlling the drone. The methodology for this study involves the Crazyflie 2.1 drone platform equipped with a frontal camera and an Inertial Measurement Unit (IMU). The drone’s flight data, including position, orientation, and velocity, is continuously monitored and analyzed using Kalman Filter (KF). This algorithm processes the data from the camera and the IMU to estimate the drone’s state accurately. Based on these estimates, corrective commands are generated and sent to the drone’s control system to maintain stability. To evaluate the effectiveness of the proposed system, a series of flight tests are conducted under different environmental conditions and flight manoeuvres. Performance metrics such as drift, level of oscillations, and overall flight stability are analyzed and compared against baseline experiments with conventional stabilization methods. Additional simulated tests are carried out to study the effect of the communication delay. The expected outcomes of this research will contribute to the advancement of drone stability systems. If successful, the implementation of a frontal camera and sensor fusion can provide a cost-effective and lightweight solution for stabilizing drones.