Illuminant estimation is a process of finding light source color chromaticity to achieve color constancy. A number of combinational color constancy algorithms that combine the estimates of multiple statistic- and learning-based unitary algorithms now appear in the literature. Since the unitary algorithms fail to estimate the illuminant for a wide range of scenes, the combination method aims to fuse the estimate of different unitary algorithms to achieve robust illuminant estimation. The traditional combinational method either uses a static weight to combine the estimate of unitary algorithms or choose a best unitary algorithm for the input image. The former one fails due to the use of static weight which is biased to the dominant scenes in training data and the second one has the difficulty to train a multi-class model with limited training data. This article addresses the limitation of combinational methods and proposes a novel multi-class dynamic weight model. The proposed method classifies images into different groups and a distinct dynamic weight generation model (DWM) is used by each group for the generation of weight to combine the estimate of unitary algorithms. The DWM proposed in this article generates the combination weight using an image feature and a static weight coefficient. Therefore, the weight changes with the image characteristics. Since the proposed method generates the combinational weight based on the image feature and it uses different DWMs for each image group, the proposed method is able to successfully combine the estimate of unitary algorithms for a wide range of scenes. Extensive experiments on Gehler–Shi and NUS-8 camera color constancy benchmark data set shows the proposed method is able to improve the performance by nearly 37% and 62%, respectively.