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Accurately predicting optimal continuous positive airway pressure (CPAP) using traditional PSG parameters remains challenging. Respiratory endotypes derived from PSG can reflect the pathophysiological mechanisms of diseases to a certain extent and are expected to provide a novel perspective for individualized pressure prediction. 114 Chinese adults with obstructive sleep apnea (OSA) underwent two overnight in-laboratory PSG recordings. Optimal CPAP pressure was defined by standardized manual titration performed on the second night. The analysis incorporated three sets of variables: respiratory endotypes, anthropometric indicators, and PSG respiratory parameters. After initial correlation screening, significant variables were entered into a stepwise multiple linear regression model. The dataset was randomly split into training (80%) and validation (20%) sets to assess predictive performance. A total of 114 participants (15 mild, 38 moderate, 61 severe OSA) were included. The prediction model included loop gain (LG1), ventilation capacity (Vpassive, Vmin), BMI, apnea index (AI), mean respiratory event-related oxygen desaturation (RE-OD-Mean), longest apnea duration (Ap-Dur-Long), and respiratory-related arousal index (RR-ArI): CPAP = 0.708 + 1.804×LG1 + 0.023×Vpassive - 0.015×Vmin + 0.117×BMI + 0.003×AI - 0.074×RE-OD-Mean + 0.019×Ap-Dur-Long + 0.043×RR-ArI. The final model demonstrated acceptable predictive performance in the internal validation set (R² = 0.497; RMSE = 1.507 cmH₂O). Among the retained predictors, LG1 showed the largest effect size, with each 1-unit increase associated with an approximately 1.8 cmH₂O higher predicted CPAP pressure. Integrating respiratory endotypes with anthropometric indicators and PSG respiratory parameters provides a mechanism-based framework for CPAP pressure prediction. Collapsibility traits (Vpassive, Vmin) and ventilatory control stability (LG1) substantially enhance predictive accuracy, offering a more individualized approach to PAP therapy.