It is crucial to accurately and efficiently predict transient particle transport in indoor environments in order to improve air distribution design and reduce health risks. For the steady-state indoor airflow, our previous studies have found that the integrated fast fluid dynamics (FFD) + Markov chain model increased the speed of calculation by around seven times compared to the combination of computational fluid dynamics (CFD) + Eulerian model and CFD + Lagrangian model, while achieving the same level of accuracy. However, the indoor airflow could be transient, if there was a human behaviour involved like coughing or sneezing, opening the door, and supplying the air periodically. Therefore, this study developed an FFD + Markov chain model solver for predicting transient particle transport in transient indoor airflow in OpenFOAM. This investigation used transient particle transport in a ventilated two-zone chamber to validate the model. In this case, the validation used experimental data from literature and showed that the predicted particle concentration by FFD + Markov chain model matched well with the experimental data. Besides, it had similar accuracy as the FFD + Eulerian model and the CFD + Eulerian model. The FFD + Markov chain model requires a similar computational time with the FFD + Eulerian model if the same time step size was used.