In order to reduce the rate of turnover and maintain valuable talent, research has been emphasized on employee retention strategies. High turnover could increase the cost of recruiting new staff members, loss of organizational knowledge, and interruption of team dynamics. Therefore, making accurate predictions for the potential turnover of an employee will be possible with promising predictive analytics by machine learning models. This study uses the most advanced machine learning models, such as artificial neural network (ANN), XGBoost, recurrent neural network (RNN), and random forest (RF), to predict employee turnover from a rich dataset that includes employee demographics, tenure, job satisfaction, performance ratings, compensation details, work environment factors, and feedback. The data was sourced from HR analytics repositories, industry surveys, and Kaggle datasets for training the models. The results indicate that ANN produces the highest accuracy in predicting employee turnover, that is 96.76% followed by XGBoost, RNN, and random forest, which achieved 92.3%, 90.23%, and 86.5%, respectively. Furthermore, the performance of all models increased gradually with the increase in epochs, indicating that the models have learned well from the data. The AUC-ROC and confusion matrix analysis are supportive of the prediction ability of the models. The findings of this research have significant applications for organizations looking to develop data-driven employee retention strategies. In doing so, organizations can proactively identify at-risk employees, allowing them to target specific retention measures and improve overall workforce stability. This research sets the stage for future developments in predictive analytics for human resource management.