As the primary implementers of educational activities, teachers' sense of well-being directly shapes their career sustainability and quality of life, while also exerting an indirect influence on students. In recent academic research, a shift has occurred toward adopting more robust methodological approaches, such as machine learning, to identify critical predictors of teachers' workplace well-being (WWB). The present study evaluated four distinct machine learning algorithms and ultimately selected the optimal model (random forests) to identify factors associated with educators' WWB. Complementarily, network analysis was employed to examine the associations between these predictive factors and WWB, aiming to inform targeted intervention strategies for future application. A total of 4,081 Chinese teachers participated in this study. Through latent profile analysis, these participants were categorized into three distinct subgroups: the "low WWB" profile, the "medium WWB" profile, and the "high WWB" profile. The random forest model, which demonstrated satisfactory performance, identified the top 10 predictive features from 19 variables linked to WWB. Results from the network analysis revealed notable differences in core predictive nodes across the three WWB subgroups. Specifically, increasing structural job resources emerged as the core node in the low WWB subgroup; role values functioned as the central variable in the medium WWB subgroup; and increasing challenging job demands stood out as the primary central node in the high WWB subgroup. Additionally, a directed acyclic graph was constructed to model the potential causal relations among the items under investigation. Furthermore, these key identifying factors provide valuable insights for designing future intervention programs tailored to teacher groups with medium and low levels of WWB.