Abstract: Modeling the mass density of the thermosphere is essential for understanding the upper atmospheric dynamics and for supporting satellites and the space station. Such modeling has traditionally relied on either empirical approaches or first-principles physics-based frameworks. The empirical models are computationally efficient with relatively lower accuracy, whereas the physics-based models are more accurate with the cost of computation time. In this study, a data-driven deep learning model based on a modified U-Net architecture is proposed to estimate the global thermospheric mass density at altitudes of 100 to 500 km. This model directly utilizes input features including time, spatial coordinates, geomagnetic indices, the F_10.7 solar flux, and the solar wind speed. To improve the model performance, we have introduced three main components: a gated recurrent unit-enhanced attention mechanism for spatially adaptive feature refinement, a height-adaptive normalization technique to mitigate altitude-induced bias, and a hybrid loss function combining mean absolute error with Laplacian loss to preserve both the global structure and fine-scale details. The proposed model achieves accuracy comparable to physics-based models such as the Thermosphere–Ionosphere–Electrodynamics General Circulation Model (TIEGCM), with percentage errors typically below 5%, while the simulation time has been dramatically reduced from tens of minutes to a few seconds. This framework provides an efficient and accurate tool for reconstructing the global thermospheric density and can potentially be utilized for real-time estimation of the thermosphere density under varying geomagnetic and solar conditions.