This thesis presents a parameter identification methodology for UAV powertrain system consisting of a brushless DC (BLDC) motor and propeller. Accurate mathematical modeling of BLDC motor-propeller systems is essential for advanced control design, performance optimization, and fault detection in unmanned aerial vehicle applications. The identification procedure employs a least squares method to estimate the unknown parameters of the powertrain mathematical model from experimental data. The least squares approach minimizes the squared difference between measured and estimated system responses, providing optimal parameter estimates that capture the dynamic behavior of the BLDC motor-propeller combination. The identification requires experimental measurements of key system variables including rotor speed, motor current, and control inputs.