Forest biomass reflects the carbon storage capacity of forest ecosystems. Although remote sensing-based biomass estimation techniques have become increasingly mature, the issue of signal saturation in optical remote sensing still requires further investigation. This study was conducted in the Picea schrenkiana forest of the Ili River Valley in the western Tianshan Mountains. By integrating multimodal data from ICESat-2 LiDAR and GF-6 optical imagery, we developed machine learning and deep learning models to achieve high-precision biomass estimation. Based on forest management inventory data, we extracted spectral and textural features from GF-6, along with canopy structure attributes derived from the four acquisition modes (day/night, strong/weak beams) of ICESat-2. After correlation-based feature selection, LightGBM, CatBoost, and TabNet models were trained and compared. The results showed that models integrating multi-source data significantly outperformed those based on a single data source. The TabNet model not only achieved high estimation accuracy but also provided clear feature importance rankings, with ICESat-2-derived canopy height percentiles and GF-6 red-edge vegetation indices contributing most significantly to the biomass estimation of Picea schrenkiana. These findings demonstrate the feasibility of synergistically utilizing domestic high-resolution satellites and multi-mode spaceborne LiDAR for forest biomass estimation in arid regions, providing an effective technical reference for accurate carbon sink monitoring of specific tree species in forest areas.