We developed and evaluated a mathematical model-based method to monitor cardiovascular health and estimate risk predictors from two peripheral cuff oscillation measurements. The model structure was established by studying tube-load models individually augmented with a gain, Voigt model, and standard linear solid model to best capture the relationship between carotid tonometry and cuff waveforms at the upper arm and ankle. The arm-cuff interface was better modeled with increasing viscoelasticity but not as much for the ankle-cuff interface. Next, model-estimated ankle blood pressure waveforms were used to formulate a matrix equation for estimating wave reflection. Subsequently derived risk predictors were adequately correlated with those from reference methods. Finally, subject-specific central blood pressure waveforms were estimated from two cuff oscillation signals via multichannel blind system identification. The model estimated central arterial blood pressure waveforms with good accuracy with a median RMSE of 3.08 mmHg and IQR of 1.71 mmHg.