BACKGROUND: Obstructive sleep apnea (OSA) is a prevalent and potentially severe sleep disorder characterized by repeated interruptions in breathing during sleep. Machine learning models have been increasingly applied in various aspects of OSA research, including diagnosis, treatment optimization, and developing biomarkers for endotypes and disease mechanisms. METHODS: This narrative review examines data extracted from 254 scientific publications published between 2018 and 2023 across a wide spectrum of research efforts, from diagnostic algorithms to treatment and patient management strategies. We evaluated the landscape of machine learning in OSA research by assessing the techniques used, application areas, model evaluation strategies, and dataset characteristics across studies. RESULTS: Our analysis revealed that the majority of machine learning applications focused on OSA classification and diagnosis, utilizing various data sources such as polysomnography, electrocardiogram data, and wearable devices. Deep learning models were the most popular, followed by support vector machines, with classification tasks being the most common. We also found that study cohorts were predominantly overweight males, with an underrepresentation of women, younger obese adults, individuals over 60 years old, and diverse racial groups. Many studies had small sample sizes and limited use of robust model validation. CONCLUSION: Our findings highlight the need for more inclusive research approaches, starting with adequate data collection for better generalizability of machine learning models in OSA research. Addressing these demographic gaps and methodological opportunities is critical for ensuring more robust and equitable applications of artificial intelligence in healthcare.