This paper addresses the problem that machine learning systems operate under the assumption that training and test data are sampled from fixed probability distributions, but in practice this assumption is rarely confirmed due to changes in data acquisition conditions. Therefore, we study how to perform unsupervised domain adaptation with minimal access to data with new conditions to learn a model that is robust to changes in data distribution. In particular, we analyze distribution changes by utilizing optimal transport, which allows mapping between domains, but to address the high computational cost of existing optimal transport methods, we explore optimal transport between Gaussian mixture models (GMMs). Using GMMs effectively reduces computational complexity, and we show that it is more efficient than existing shallow domain adaptation methods through nine benchmarks and a total of 85 adaptation tasks, and that it scales well with respect to the number of samples (n) and dimensionality (d).