The growing interest in the utilization of Unmanned Aerial Vehicles (UAVs) demands minimizing the costs of robot maintenance, where one of the main aspects relates to energy consumption. This manuscript presents a novel approach to create an energy consumption model for UAVs. The authors prove, based on experimentally collected data using a drone carrying various payloads, that Machine Learning (ML) algorithms allow to sufficiently accurately estimate a power signal. As opposed to the classical approach with mathematical modeling, the presented method does not require any knowledge about the drone’s construction, thus making it a universal tool. Calculated metrics show the Decision Tree is the most suitable algorithm among eight different ML methods due to its high energy prediction accuracy of at least 97.5% and a short learning time which was equal to 2 ms for the largest dataset.