Climate change has presented considerable challenges in the management of urban forests and trees. Varieties of studies have predicted the potential changes in species distribution by employing single-algorithm species distribution models (SDMs) to investigate the impacts of climate change on plant species. However, there is still limited quantitative research on the impacts of climate change on the suitable distribution ranges of commonly used urban tree species. Therefore, our study aims to optimize traditional SDMs by integrating multiple machine learning algorithms and to propose a framework for identifying suitable distribution ranges of urban trees under climate change. We took Michelia chapensis, a tree species of particular significance in southern China, as a pilot tree species to investigate the evolution of its suitable distribution range in the context of two future climate scenarios (SSP126 and SSP585) across four periods (2030s, 2050s, 2070s, and 2090s). The findings indicated that the ensemble SDM showed strong predictive capacity, with an area under the curve (AUC) value of 0.95. The suitable area for Michelia chapensis is estimated at 15.9 × 10⁵ km² currently and it will expand in most areas under future climate scenarios according to the projection. However, it will contract in southeastern Yunnan, central Guangdong, the Sichuan Basin, northern Hubei, and Jiangxi, etc. The central location of the current suitable distribution area is located in Hengyang, Hunan (27.36° N, 112.34° E), and is projected to shift westward with climate change in the future. The migration magnitude is positively correlated with the intensity of climate change. These findings provide a scientific basis for the future landscape planning and management of Michelia chapensis. Furthermore, the proposed framework can be seen as a valuable tool for predicting the suitable distribution ranges of urban trees in response to climate change, providing insights for proactive adaptation to climate change in urban planning and landscape management.