In this article, the temperature of biological tissues is estimated during hyperthermia therapy, while accounting for uncertainties in the bioheat transfer problem and in the available measurements. A state estimation problem is solved with the Steady-State Kalman Filter. The Pennes bioheat transfer model and the PRF-Shift Magnetic Resonance Thermometry are used as evolution and observation models, respectively. Instead of using the direct inversion of the measured data as with the PRF-Shift Magnetic Resonance Thermometry, the state-estimation framework allows for enhancing the spatial resolution of the estimated temperature variation and reducing the related uncertainties. Since the time consuming steps of the Steady-State Kalman Filter can be performed offline, the recursive solution of the state estimation problem is performed with computational times smaller than the simulated physical times. Synthetic measurements are used for the state estimation problem in a region of the human forearm, for radiofrequency and laser-diode heat sources of the hyperthermia therapy.