Cardiovascular diseases (CVDs) are the primary cause of death in the world. The development of easy-to-use and non-invasive monitoring CVDs’ diagnosis methods is crucial. Among the key parameters in the cardiovascular system is arterial stiffness. An increase in arterial stiffness is considered a primary risk factor for CVDs. Although arterial stiffness can be assessed non-invasively by measuring the carotid-to-femoral pulse wave velocity (cf−PWV), which is considered as a gold standard for arterial stiffness measurement, the clinical process of assessing this parameter is very intrusive and complicated. This paper investigated the potential of estimating (cf−PWV) from distal photoplethysmogram (PPG) waveforms using regression technique based on a multilayer perceptron. Functionally, PPG offers a simple, reliable, low-cost technique to measure blood volume change and hence assess cardiovascular function. In this work, we identify and select features from the timing fiducial points-based PPG, its first, second, and third derivative waveforms. The in-silico validation shows promising results and satisfactory accuracy. It demonstrates good estimation performances with an R2 (correlation coefficient) around 0.95 and MAPE (mean absolute percentage error) less than 2.22% based on features extracted from PPG at the brachial artery level, an R2 around 0.98 and MAPE less than 1.71% based on features extracted from PPG at the radial artery level and R2 around 0.97 and MAPE less than 1.88% based on features extracted from PPG at the digital artery level.