In this paper, we propose a new head pose estimation technique based on Random Forest (RF) and Multi-scale Block Local Block Pattern (MB-LBP) features. In the proposed technique we aim to learn a randomized tree with useful attributes to improve the estimation accuracy and tolerance of occlusions and illumination. Precisely, a number of MB-LBP feature spaces are generated from a face image, and random inputs and random features such as the MB-LBP scale parameter and the block coordinate in the pool are used for building the tree. Furthermore we develop a split function considering the properties of the uniform LBP, applied to each internal node of the tree to maximize the information gain at that node. The randomized trees put together in RF are used for the final decision in a Maximum-A-Posteriori criterion. Experimental results demonstrate that the proposed technique provides impressive performance in the head pose estimation in various conditions of illumination, poses, expressions, and facial occlusions.