Unmanned Aerial Vehicles (UAVs) require precise positioning capabilities for autonomous navigation in complex environments. This paper proposes a comprehensive machine learning-based error compensation framework that integrates deep neural networks (DNN), long short-term memory networks (LSTM), and intelligent multi-sensor fusion. The method addresses complex error propagation dynamics in UAV positioning systems by learning nonlinear error patterns from multi-sensor data. Experimental validation using real flight data from the EUROC dataset demonstrates 35.2% RMSE improvement over traditional filtering methods, with 92.3% error type identification accuracy and real-time processing capability (2.3ms per sample). The system effectively handles GPS-denied environments and maintains sub-meter positioning accuracy through sensor fusion, providing a robust framework for enhanced UAV navigation reliability.