Smart buildings focus on providing optimal comfort for the occupant with reduced energy consumption. Better occupant prediction and behavior analysis can significantly reduce building energy usage. Human being is an important parameter in the building control process and his comfort is paramount. Therefore, occupant modeling is critical in improving building efficiency while maintaining indoor comfort. Although, there are many different algorithms developed for occupancy modeling, Markov chain and its derivative models are extensively used because of their simplicity, flexibility, and prediction efficiency. In this context, this paper proposes a state of the art review focused on Markov chain and its derivative models for occupant modeling.