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Fatigue can result in a decrease in the driver's level of alertness and reaction time, threatening the safety of road users. This study focuses on developing a driver fatigue detection system that relies on eye blink measurements using the Manhattan Distance method. The main goal is to improve driving safety by accurately detecting the driver's fatigue level. The proposed system measures eye blinks and uses Manhattan Distance to transmit the difference between normal and drowsiness-affected eye blink patterns. Experiments show that the Manhattan Distance method effectively identifies driver fatigue based on eye blink patterns, achieving a satisfactory accuracy rate of 87.5%. Experimental results show that the Manhattan Distance method is effective in identifying driver fatigue based on eye blink patterns, with a satisfactory level of accuracy. With this success, this system could potentially be implemented in vehicles as part of a safety system, helping to reduce the risk of accidents due to driver fatigue in real-time.