Employee performance observation is an important role to play in HR analytics in an effort to optimize the productivity and staffing alignment. A real-time detection and assessment of employees using Recurrent Neural Networks (RNN) that can detect the behavior of an employee and establish patterns of productivity is brought forth by this study. The Employees Performance for HR Analytics dataset is utilized, such as task completion rate, work efficiency, absenteeism pattern, and work rate. Feature selection with Principal Component Analysis (PCA) and removal of outliers with Isolation Forest are utilized for improving the quality of the data. Long Short-Term Memory (LSTM) with Attention Mechanism is utilized to extract features that have temporal dependences and behavior insight. Long Short-Term Memory (LSTM) with Attention Mechanism gets a 94.27% accuracy, 92.83% precision, 91.45% recall, 92.12% F1-score, and ROC-AUC of 95.36%, which is better prediction performance. This setup enables HR professionals to monitor the performance of employees in real-time, detect inefficiencies, and improve engagement programs. Embedding deep learning-based monitoring provides a scalable and responsive solution to enable data-driven decisions by HR. By the recognition of productivity trends and the forecasting of workforce behavior, this system maximizes the workforce, minimizes the risk of attrition, and optimizes performance evaluation. The envisaged framework delivers a cost-effective, real-time HR analytics platform, revolutionizing staff management and optimizing organizational productivity.