Comprehending the population trend and understanding the distribution range dynamics of species is necessary for global species protection. Recognizing what causes dynamic distribution change is crucial for identifying species’ environmental preferences and formulating protection policies. Here, we studied the rear-edge population of the flagship species, giant pandas (Ailuropoda melanoleuca), to 1) assess their population trend using their distribution patterns, 2) evaluate their distribution dynamics change from the 2nd (1988) to the 3rd (2001) surveys (2–3 Interval) and 3rd to the 4th (2013) survey (3–4 Interval) using a machine learning algorithm (The Extremely Gradient Boosting), and 3) decode model results to identify driver factors in the first known use of SHapley Additive exPlanations. Our results showed that the population trends in Liangshan Mountains were worst in the 2^nd survey (k = 1.050), improved by the 3^rd survey (k = 0.97), but got worse by the 4^th survey (k = 0.996), which indicates a worrying population future. We found that precipitation had the most significant influence on distribution dynamics among several potential environmental factors, showing a negative correlation between precipitation and giant panda expansion. We recommend that more study is required to understand the micro-environment and animal distribution dynamics. We provide a fresh perspective on the dynamics of Giant Panda distribution, highlighting novel focal points for ecological research on this species. Our study offers theoretical underpinnings that could inform the formulation of more effective conservation policies. Also, we emphasize the uniqueness and importance of the Liangshan Mountains giant pandas as the rear-edge population, which is at a high risk of population extinction.