Understanding the influence of urban morphology on Land Surface Temperature (LST) is essential for urban planning, development, and mitigating the urban heat island effect. Leveraging high-resolution remote sensing data, this study systematically extracted 64 2D urban morphological parameters (UMPs) and 28 3D UMPs, along with their corresponding summer and winter LST data, at both the grid level (using a 30 m × 30 m grid as the minimum unit) and the block level (using an urban block as the minimum unit). The 2D UMPs were derived from landscape indices of land cover, while the 3D UMPs included 3D building-related UMPs (BUMPs) and tree-related UMPs (TUMPs). Ultimately, multiple statistical methods were employed to investigate the complex mechanisms through which these 2D and 3D UMPs influence LST across summer and winter. This study showed the following results: (1) Most 2D and 3D UMPs significantly correlated with LST in both seasons at the grid/block levels, with stronger correlations at block level. (2) Stepwise regression revealed that combining 2D and 3D UMPs enhanced LST explanation, achieving R² = 70.9% (summer) and 65.7% (winter) for the entire area, with consistent results in built-up zones. (3) Relative importance analysis identified 35 (summer) and 28 (winter) influential features, which were ranked as follows: 2D UMPs > 3D BUMPs > 3D TUMPs. This highlights 2D UMPs’ dominance while confirming 3D UMPs’ significance. These findings emphasize the need for integrated 2D and 3D urban design, considering both planar layouts and vertical configurations of buildings/vegetation. This study provides practical guidance for thermal environment mitigation and sustainable urban development through optimized spatial planning.