This research presents human resource management (HRM) practices using deep learning as predictive analytics for performance, recruitment, and retention. Using a dataset provided by the technology company with 482 employees, five deep learning models such as recurrent neural networks (RNN), long short-term memory networks (LSTM), bidirectional LSTM (BiLSTM), convolutional neural networks (CNN), and transformer networks are evaluated. The results indicate that LSTM achieved an accuracy of approximately 85%, while BiLSTM achieved a slightly higher accuracy of 87%, effectively covering the temporal dependencies of employee performance data. CNN also provides 88% accuracy when analyzing recruitment data in comparison with the Transformer Network, which achieved 90% accuracy due to its ability to identify complex data structures. The results reveal the high capacity of deep learning models to strengthen HRM strategies by acquiring a deeper understanding of employee behavior and enhancing decision-making mechanisms. The research underlines the advantage of advanced analytics in HR practices over conventional methodologies that help to create an engaged and productive workforce, thus helping organizations do their best as they face a challenging competition landscape.