Under the current difficult employment situation, many companies are facing the problem of employees’ turnover. Identifying employees turnover intentions, 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 examine the determinants of employee turnover intention in real-world corporate settings, with the goal of enhancing firms’capability to predict such intentions and address this issue. This study first identifies imperative predictive variables through feature engineering and employs machine learning algorithms. It then leverages the random forest algorithm to determine 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, the predictive model is applied to test its validity again across the entire company. Moreover, this research tests the correlation between turnover features and turnover intention and finds that these relationships are consistent with previous studies. This research not only comprehensively explores the influencing factors of employee turnover intention using machine learning but also provides useful advice for companies to reduce employee turnover by intervening early and expressing concern in advance.