Accurate inversion of low altitude atmospheric temperature and humidity is crucial for weather forecasting and climate monitoring. This letter introduces the MR-TH method, a deep learning approach that uses convolutional neural networks and Transformer architecture to invert low altitude three-dimensional atmospheric temperature and humidity distribution from airborne microwave radiometer data. By capturing nonlinear relationships and spatial correlations, MR-TH improves the inversion accuracy of traditional methods. This network is trained and validated using onboard flight data, reanalysis products, and radiosonde measurements. The results indicate that the mean square error (MSE) of temperature inversion for MR-TH is 0.3-1.5 K and the humidity MSE is 0.2-2.0 g/kg, with an accuracy improvement of over 15% compared to the BP neural network method within the range of 1-5 km altitude. MR-TH also shows a high correlation (>90%) with radiosonde data. MR-TH provides a feasible solution for improving the accuracy of atmospheric parameter inversion from airborne microwave radiometer observation data.