Compared to natural surfaces, urban surfaces have more complex geometric structures, leading to significant impacts of the multiple scattering effect within pixels and the neighborhood effect on the inversion results of urban land surface temperature (LST). This study proposed a novel urban LST inversion algorithm that integrates machine learning and an enhanced temperature and emissivity separation (TES) method. Finally, the proposed algorithm was applied to China’s SDGSAT-1 thermal infrared data. The algorithm comprises three key steps: First, the inversion of urban canopy brightness temperature from SDGSAT-1 data was conducted using the eXtreme Gradient Boosting (XGBoost) algorithm. Second, an enhanced TES algorithm based on the sky view factor (SVF) was developed to account for urban geometry, enabling high-precision urban LST inversion. Third, the accuracy of the inversion algorithm was assessed and applied to the urban area of Beijing. The results demonstrate that inversion using an XGBoost algorithm and a split-window algorithm yielded root mean squared errors (RMSEs) of approximately 0.2 K and 1.2 K, respectively. The LST RMSEs with and without available water vapor data were determined at 0.36 K and 0.73 K, respectively; and the LSE RMSEs under three bands were 0.020/0.026, 0.018/0.023, and 0.020/0.023, respectively. The differences in the LST inversion results derived using the original and improved TES algorithm ranged from 0 to 1.86 K.