Under the current difficult employment situation, many companies are still facing the question of employees’ turnover. Finding employees’ turnover intention, understanding the underlying causes, and implementing preemptive interventions are pivotal strategies to mitigate such intentions. Although prior studies have delved into the factors precipitating turnover intentions, their findings lack robust predictive power. This study aims to scrutinize the determinants of employee turnover intention, thereby enhancing corporate capability to anticipate and address this issue within the organizational contexts. This study, first, identifies imperative predictive variables through feature engineering and employs machine learning algorithms. Second, it leverages the random forest algorithm to ascertain the hierarchy of turnover determinants and develop a forecasting model. Meanwhile, a quasi-experimental approach is utilized to validate the predictive model in a corporate setting. Finally, applying the predictive model to test the validity again in the whole company. Moreover, this research tests the correlation between turnover features and turnover intention, and finds these relationships are consistent with previous research. This research not only comprehensively explores the influence factors of employees’ turnover intention by using machine learning, but also provides useful advice for companies to reduce employees' turnover by intervening and expressing concern in advance.