The ventricular assist device (VAD) provides supports for cardiac function and is used in the long term by many patients on the heart transplant waiting list. Thus, during VAD operation, it is ideal to monitor circulatory status and cardiovascular parameters online to reduce complications, detect abnormal conditions early and control the ventricular assist device. However, wearing additional sensors in the body for monitoring is not ideal, and it is desirable to obtain various types of information about the body using as few and easily measurable sensors as possible. In this study, we propose a method for real-time estimation of the circulatory state [left ventricular pressure (LVP), aortic pressure (AoP), aortic flow (AoF)], and cardiovascular parameters, such as peripheral vascular resistance, and determination of valve opening using the estimated AoF. The proposed method utilizes information generated by the VAD, which is easy to measure. First, the method approximates the heart and circulatory system using an electric circuit model based on the Windkessel model. This model is represented by a state-space model with pump flow rate and pump inlet pressure as inputs and pump outlet pressure as output. In the next step, the model parameters are estimated using an unscented Kalman filter, which can handle nonlinear functions, enabling the estimation of circulatory systems. Validation using data from a hybrid simulated circulatory system and animal studies demonstrate that the normalized root mean square errors (nRMSE) for LVP, AoP, and AoF were within 30%, 13%, and 7%, respectively. Moreover, it was possible to estimate opening of the aortic valve. In particular, data from the animal experiments illustrated that the proposed method can accurately estimate changes in the circulatory state by taking medication, highlighting the effectiveness of the proposed method.