Aims: Peripheral pulse waves contain rich hemodynamic information and can be acquired noninvasively with ease. However, estimating systemic vas-cular resistance from peripheral waveform morphology remains challenging because vascular resistance is influenced by multiple interacting cardiovas-cular factors. In Silico pulse wave databases provide a controlled framework for developing waveform-based resistance models, but external validation in real-world recordings is essential for clinical translation. Therefore, we inves-tigated whether pulse wave models trained on a virtual database can capture resistance-related variation in Mobil-O-Graph waveform data. Methods: Four waveform-based models were developed using the in silico Pulse Wave Database (PWDB) to estimate systemic vascular resistance (SVR) from brachial and radial pressure waveforms: ridge regression, elastic net, support vector regression, and an optimized multilayer perceptron. For exter-nal validation, peripheral pulse wave images from Mobil-O-Graph recordings were processed to extract beat-level waveforms, from which segment-level median templates were constructed. Each median template was then ampli-tude-calibrated using the corresponding systolic and diastolic blood pressure values to generate pressure-scaled waveforms. The calibrated Mobil-O-Graph templates were subsequently input into the PWDB-trained models to obtain predicted SVR values, which were compared with device-derived total vascular resistance (TVR) to assess external agreement. Results: Among the four regression models, elastic net showed the best overall agreement with device-derived TVR. The mean difference between predicted_SVR_z and TVR_z was near zero, indicating minimal bias. The median z-score difference was 0.035, with MAE_z of 0.955, RMSE_z of 1.219, and median absolute z-difference of 0.809. In threshold-based analy-sis, 32.9% of segments were within 0.5 SD, 60.8% within 1.0 SD, 80.4% with-in 1.5 SD, and 89.3% within 1.96 SD. These findings suggest that the PWDB-derived elastic net model captured resistance-related variation in Mobil-O-Graph waveforms with moderate but consistent agreement with TVR. Conclusion: Elastic net best captured resistance-related variation, support-ing translational use of PWDB-trained models in Mobil-O-Graph waveforms.