In order to develop a high-performance model for predicting the spontaneous combustion tendency of coal, this study utilizes multi-indicator gas and industrial analysis parameters of coal. It employs random forest, neural network, support vector machine, and Stacking stacking methods to predict the spontaneous combustion temperature and natural ignition period of coal in order to evaluate its spontaneous combustion tendency. The findings indicate that the Stacking stacking method-based prediction model exhibits superior generalization ability. Furthermore, feature importance analysis reveals that volatile matter and ethylene demonstrate strong correlations with predicting the natural ignition period and spontaneous combustion temperature of coal respectively. Analysis of model performance indicators suggests that increasing data volume significantly enhances the generalization performance of random forest, neural network, support vector machine, and Stacking stacking methods in predicting coal spontaneous combustion temperature. However, simply increasing data volume has limited impact on improving the prediction model for coal's natural ignition period; instead, exploring new features is essential for enhancing model performance. This study offers valuable insights into predicting coal spontaneous combustion tendency and optimizing predictive models.