Innovative possibilities in healthcare and personal well-being monitoring are enabled by a system integrating EEG sensors, Arduino microcontrollers, and Python scripts for real-time drowsiness detection. The device captures brain wave signals, processes them, and analyzes patterns indicating reduced alertness, utilizing EEG sensors to record signals, Arduino for data processing, and CP2102 for data transfer to a computer. Python scripts analyze EEG signals to detect patterns such as suppressed alpha waves or increased theta waves, signaling drowsiness. This system has diverse applications, including monitoring patients recovering from anesthesia, assessing sleep quality in individuals with sleep disorders, detecting neurological conditions like narcolepsy, and tracking drowsiness in drivers, pilots, or operators of heavy machinery. It can also optimize sleep stages for better rest quality and enhance cognitive performance. The system offers advantages such as immediate intervention, noninvasive operation, affordability, portability, and ease of integration with existing systems, while achieving high accuracy in detecting drowsiness-related brain wave patterns. Future opportunities include integration with wearable devices, advanced machine learning for improved pattern recognition, and multi-modal sensing, showcasing the potential to transform healthcare and personal wellness monitoring for safer and healthier lives.