The growing prevalence of sleep disorders and the increasing awareness of sleep’s impact on health have accelerated the demand for personalized sleep monitoring solutions. Traditional methods such as polysomnography, while accurate, are expensive, invasive, and impractical for continuous home use. Recent advances in artificial intelligence (AI) offer a transformative approach to sleep tracking by enabling personalized, non-intrusive, and data-driven solutions that adapt to individual needs. This paper explores the integration of AI technologies into wearable and ambient sleep tracking systems, focusing on how machine learning algorithms analyze physiological signals such as heart rate, respiratory patterns, movement, and audio cues to accurately predict sleep stages and detect anomalies. The incorporation of AI not only improves accuracy but also enables tailored feedback and behavioral interventions based on a user’s unique sleep patterns and lifestyle. Furthermore, the paper discusses the role of AI in correlating sleep data with broader health metrics, paving the way for comprehensive wellness insights. Challenges related to data privacy, model transparency, and device interoperability are also addressed. Overall, the fusion of AI and personalized sleep tracking represents a significant leap forward in consumer health technology, promising improved sleep quality and early detection of sleep-related disorders through intelligent, adaptive monitoring systems.