Acquiring the systems' model is a significant stride of controller design and development. Nevertheless, it is arduous sometimes to estimate a real-world system model such as a quadcopter UAV, which has non-linearity, difficult-to-measure, and complex characteristics. This condition caused system identification plays a crucial role in the quadcopter modeling. With the ease of obtaining the experimental flight data directly from the quadcopter, hence modeling of the quadcopter using system identification now is handy. Conforming to that, the development of machine learning algorithmsspecifically on the deep learning field, driven new perspectives on the system identification approach. In this paper, several deep learning architectures were applied to identify the quadcopter UAVsystem. Overall, results show that the CNN-LSTM model was the top-performed architecture with the average tested MSE and MAE equal to 0.0002 and 0.0030.