Mitigation of the heat island effect is critical due to the frequency of extremely hot weather. Urban street greening can achieve this mitigation and improve the quality of urban spaces and people’s welfare. However, a clear definition of street green space morphology is lacking, and the nonlinear mechanism of its cooling effect is still unclear; the interaction between street green space morphology and the surrounding built environment has not been investigated. This study used machine learning, deep learning, and computer vision methods to predict land surface temperature based on street green space morphology and the surrounding built environment. The performances of the XGBoost, LightGBM, and CatBoost models were then compared, and the nonlinear cooling effects offered by the street green space morphology were analyzed using the Shapley method. The results show that streets with a high level of green environment exposure (GVI > 0.4, NDVI > 4) can accommodate more types of green space morphology while maintaining the cooling effect. Additionally, the proportion of vegetation with simple geometry (FI < 0.2), large leaves (FD < 0.65), light-colored leaves (CSI > 13), and high leaf density (TDE > 3) should be increased in streets with a low level of green environment exposure (GVI < 0.1, NDVI < 2.5). Meanwhile, streets with highly variable building heights (AFI > 1.5) or large areas covered by buildings (BC > 0.3) should increase large leaf vegetation (FD < 0.65) while decreasing dark leaf vegetation (CSI < 13). The study uses machine learning methods to construct a nonlinear cooling benefit model for street green space morphology, proposes design recommendations for different street green spaces that consider climate adaptation, and provides a reference for urban thermal environment regulation.