Accurate blood pressure (BP) estimation is critical for cardiovascular health monitoring, with cuffless methods like Pulse Arrival Time (PAT) emerging as non-invasive, continuous alternatives. However, the reliability of PAT-based models for BP estimation remains uncertain due to challenges in model calibration and the complexity of BP regulation. This study investigates the performance of various PAT-based models in estimating systolic (SBP) and diastolic (DBP) BP using the AuroraBP dataset, which contains ambulatory data from 547 participants. The best-performing PAT-based model for SBP was M5-PATS with a mean absolute error (MAE) of 14.23 mmHg. For DBP, it was M2-PATo with a MAE of 9.67 mmHg. However, these results did not meet the accuracy criteria outlined by international standards. Additionally, the baseline model outperformed the M5-PATS model for SBP (MAE: 13.04 mmHg) and achieved comparable performance for DBP (MAE: 9.67). Despite promising results in previous studies, our findings highlight the poor performance of PAT-based models, particularly in dynamic and ambulatory conditions. Future research should explore more advanced approaches, such as deep learning, to capture the complexity of BP variations and provide reliable, continuous BP monitoring in real-life settings.