The health consequences of insufficient sleep are making sleep quality more important on a global scale. In this regard, links between sleep and an increased chance of getting thyroid cancer or worsening symptoms of Post Traumatic Stress Disorder have been confirmed. More research is needed in this field to understand better the consequences and how sleep quality might be improved. The present paper investigates the use of Artificial Intelligence in personalised sleep monitoring by using a Digital Twin model to assess the impact of environmental factors on sleep quality. To reach the objectives, questionnaire data was used from 1022 students enrolled at Türkiye’s Gendarmerie and Coast Guard Academy to evaluate how sleep habits affect cognitive and physical performance. The Digital Twin, a synthetic representation of each student’s sleep and performance parameters, uses AI-driven analysis to imitate real-world environmental factors to forecast sleep quality. The suggested approach considers a range of sleep-related characteristics, such as sleep length, quality, and interruptions, and makes individualized evaluations of sleep health. The findings reveal a link between environmental factors and sleep quality, stressing the importance of tailored sleep management measures. Using AI and Digital Twin technologies, this study proposes a unique strategy for recognizing and reducing the detrimental impacts of sleep deprivation, eventually leading to increased well-being and performance in high-stakes contexts like military and law enforcement training.