Semi-supervised learning has made remarkable strides by effectively utilizing a limited amount of labeled data while capitalizing on the abundant information present in unlabeled data. However, current algorithms often prioritize aligning image predictions with specific classes generated through self-training techniques, thereby neglecting the inherent relationships that exist within these classes. In this paper, we present a new approach called SaMatch, which leverages semantic relationships among classes by employing an optimal transport loss function to match distributions. We conduct extensive experiments on vision datasets like CIFAR 10/100, STL-10, and ImageNet and language datasets like Amazon Review, and Yelp Review. The empirical results show substantial improvements in our method above baseline, this demonstrates the effectiveness and superiority of our approach in harnessing semantic relationships to enhance learning performance in a semi-supervised setting.