A new portable blood flow volume (BFV) sensor is developed from the function of estimating oxygen saturation (SpO2) implemented inherently to maximize the accuracy of predicting BFV. The sensor is designed to estimate BFV in high accuracy at the arteriovenous fistula (AVF) of a hemodialysis (HD) patient based on a built artificial neural network (ANN). The BFVs measured by the proposed sensor would help greatly evaluate the AVF complications at early stage, such as infection, bleeding, stenosis, and vascular calcification, while AVFs are under long-time usage. The sensor module consists of LEDs/PDs, readout circuitry and algorithm to estimate BFV. The oxygen saturation (SpO2) is also estimated using the same hardware to serve as one of input features for the aforementioned ANN for maximize the accuracy in BFV. With using the hardware, oxygen saturation’s algorithm is built and implemented with the mean deviation (MD) and standard deviation (SD) adapted FDA standard, which is MD ∓ 1.96SD = 0.024% ∓ 1.772% < 5%. Besides, the BFV ground-truth data are obtained from a transonic HD03 dilution machine for calibrating the ANN. The accuracy in estimating BFV with and without estimating SpO2 are compared, which correspond to R2 = 0. 94,517, root mean square error = 92.199 ml/min and R2 = 0.87145, root mean square error = 141.015 ml/min, respectively. Besides, another neural network model is implemented with fixing value of an input feature of SpO2, the ANN model’s correlation reaches only 0.1554, while the related root mean square error is 493.2442 ml/min. Therefore, the results validate clearly the necessity of the inherent estimation on SpO2.