This paper presents a novel approach based on deep learning techniques for dynamic geometric camera calibration in the context of autonomous aerial navigation. Unlike traditional offline calibration methods, the proposed technique leverages convolutional neural networks (CNNs) to adjust intrinsic parameters in real time, thereby adapting to environmental and mechanical variations that occur during flight. Experimental results demonstrate that this adaptive calibration framework significantly enhances the performance of monocular visual odometry by reducing cumulative trajectory errors in dynamic environments. The findings indicate that continuous calibration driven by deep learning models is a promising solution for improving the robustness and autonomy of vision-based navigation systems in unmanned aerial vehicles (UAVs). These results also reinforce the growing potential of deep learning to replace static and manual calibration procedures in embedded real-time applications.