A new neural network (NN) is orchestrated by this study to achieve high-accuracy in blood pressure (BP) estimation by a real-time photoplethysmography (PPG). The PPG system consists of an OLED/OPD module to detect the pulsation of blood vessels, followed by a readout circuitry. The circuit is comprised of transimpedance amplifier, a digital tune high order band pass filter, programmable gain amplifier (PGA), time interleave OLED driver, micro-controller unit, and the Bluetooth transceiver. The obtained PPG signals are subsequently processed with quality checking, feature extraction, and into an NN for estimating BP. The feature extraction is assisted, by principal component analysis (PCA) to reduce the total number of input features to five with accuracy assured. 96 subjects participated in data collection for calibrating the designed NN. The resulted correlation is 0.81, while the errors for SBP and DBP are 2.00 ± 6.08 and 1.87 ± 4.09 mmHg, respectively. According to the Advancement of Medical Instrumentation (AAMI) and British Hypertension Society (BHS), a BP device in Grade A needs to control its accuracy error less than ± 8 mmHg, based on which the BP sensor developed herein are in Grade A, since the resulted errors of ± 6.08 and ± 4.09 mmHg are both less than ± 8 mmHg, showing the satisfactory performance of the BP monitor developed by this study.