Positive airway pressure therapy serves as a core therapeutic strategy for sleep-disordered breathing, with its efficacy generally evaluated via sleep breathing assessment. Nevertheless, the prevailing flow and pressure-based methods remain insufficient in both accuracy and interference resistance. Here, we propose an intelligent ventilation mask system based on the impedance humidity-sensing mechanism for long-term continuous and accurate respiratory pattern monitoring and evaluation. The integrated sensor is based on TiCT MXenes modified with dopamine-functionalized polyethyleneimine, which forms a wrinkled surface that increases the active area for water adsorption and provides a passivation effect to inhibit oxidation-induced performance degradation in humid atmospheres. The fabricated sensor exhibits high sensitivity (average of 2.29 × 10 Ω/%RH) and low humidity hysteresis (<0.83%). Even after extreme temperatures (100 °C/-20 °C) and 90-day exposure, it can maintain consistent performance. Integrated with a machine learning algorithm, the system identifies 11 respiratory patterns (5 normal and 6 abnormal) with 99.74% accuracy under ventilatory airflow and analyzes 8-hour continuous nighttime sleep data via customized software. This lays the groundwork for improving the long-term management of sleep-disordered breathing in both clinical and home settings.