Obstructive Sleep Apnea (OSA) management struggles from static retrospective adherence models which build their assessment on rigid compliance benchmarks. The study introduces an Artificial Intelligence-based framework for CPAP treatment adaptation which combines real-time biosensor data and personalized treatment stages. The system categorized patients based on therapy dynamics through unsupervised learning analysis into four stages new, adherent, attempting, and non-adherent while considering CPAP usage data and clinical histories and Monitoring survey results. The adopted monitoring system tracks a selection of digital biomarkers (e.g., HRV, ODI, RVO) in real-time to identify initial signs of therapy deviation as well as changes in physiological responses. It enables the risk scoring system to set off weekly risk evaluations and adjust cluster groupings and activate custom-made intervention plans such as digital prompts and medical survey requests alongside physician alert actions. Continuous physician feedback connections enable healthcare providers to check and modify or restructure interventions to ensure clinical accuracy and security. The system provides dynamic adjustment of biomarker approaches alongside intervention force levels which depend on patients' current therapy state and clinical complexity. The proposed framework displays promising results to detect patients at risk for dropping out and to optimize their therapy according to preliminary retrospective data analysis. The developed work provides a scalable and explainable clinical framework that will support future tests and deployments for biomarker-driven real-time CPAP personalization methods throughout the OSA care pathway.