In this paper, we compare the use of Bayesian filters for the estimation of release and reencapsulation rates of a chemotherapeutic agent (namely Doxorubicin) from nanocarriers in an acoustically activated drug release system. The study is implemented using an advanced kinetic model that takes into account cavitation events causing the antineoplastic agent’s release from polymeric micelles upon exposure to ultrasound. This model is an improvement over the previous representations of acoustic release that used simple zero-, first- and second-order release and reencapsulation kinetics to study acoustically triggered drug release from polymeric micelles. The new model incorporates drug release and micellar reassembly events caused by cavitation allowing for the controlled release of chemotherapeutics specially and temporally. Different Bayesian estimators are tested for this purpose including Kalman filters (KF), Extended Kalman filters (EKF), Particle filters (PF), and multi-model KF and EKF. Simulated and experimental results are used to verify the performance of the above-mentioned estimators. The proposed methods demonstrate the utility and high-accuracy of using estimation methods in modeling this drug delivery technique. The results show that, in both cases (linear and non-linear dynamics), the modeling errors are expensive but can be minimized using a multi-model approach. In addition, particle filters are more flexible filters that perform reasonably well compared to the other two filters. The study improved the accuracy of the kinetic models used to capture acoustically activated drug release from polymeric micelles, which may in turn help in designing hardware and software capable of precisely controlling the delivered amount of chemotherapeutics to cancerous tissue.