In this paper, we propose a practical and cost-effective system for real-time tracking and predicting alertness levels. Instead of relying on multi-electrode sensors that require complex setups and may cause discomfort, our system uses a compact, single-electrode sensor to capture EEG data. This data is then analyzed by various machine learning models to calculate an Awake Score for users. The Awake Score is also used as input for a forecasting model, which predicts the users’ alertness trends in advance. The forecasting model leverages an advanced deep learning model, Long Short-Term Memory (LSTM), to handle the EEG data and detect intricate temporal patterns in brain activity. Furthermore, we optimize the system to function with minimal electrodes while maintaining high predictive accuracy, providing a feasible solution for real-time detection of fatigue and cognitive load.