Microplastics (MPs) are ubiquitous and have various characteristics. However, their impacts on bacterial community functions in lakes remain elusive. In this study, we identified 33 different MPs features including their abundance, shape, color, size, and polymer type, from Taihu Lake, China. These features were used to construct 48 machine learning models, utilizing four types of machine learning regression algorithms, to investigate how different MP features influence human health, carbon/nitrogen cycling, and energy source-related functions of bacterial communities. The XGBoost models provided the best performance with an average R