The recombinant adeno-associated virus (rAAV) is a viral vector technology for gene therapy that is considered the safest and most effective way to repair single-gene abnormalities in non-dividing cells. However, improving the viral titer productivity in rAAV production remains challenging. The first step to this end is to effectively monitor the process state variables (cell density, GLC, GLN, LAC, AMM, and rAAV viral titer) to improve the control performance for an enhanced productivity. However, the current approaches to monitoring are expensive, laborious, and time-consuming. This paper presents an extended Kalman filter (EKF) approach used to monitor the rAAV production using the online viable cell density measurements and estimating the other state variables measured at a low frequency. The proposed EKF uses an unstructured mechanistic kinetic model applicable in the upstream process. Three datasets were used for parameter estimation, calibration, and testing, and the data were collected from the production of rAAV through a triple-plasmid transfection of HEK293SF-3F6 cells. Overall, the proposed approach accurately estimated metabolite concentrations and the rAAV production yield. Therefore, the approach has a high potential to be extended to an online soft sensor and to be classified as a cost-effective and fast approach to the monitoring of rAAV production.