With the widespread use of the internet and the development of wireless networks that transfer large data streams, the importance of assessing and controlling the quality of communication links in wireless networks has gained significant attention. By predicting link quality, energy consumption of network nodes and the overall stability of the network can be improved. One category of methods used for predicting the quality of wireless links is machine learning techniques. This paper examines the performance of ensemble methods, a type of supervised machine learning approach that has previously received less focus in the context of wireless link quality prediction. Additionally, due to the advantages of unsupervised methods that can be trained on unlabeled datasets, the performance of the k-means algorithm is also evaluated. The results show that ensemble algorithms are highly effective in predicting the quality of communication links in wireless networks. Among the ensemble methods, Gradient Boosting achieved the best performance with an F1 score of 95.79, while the k-means method demonstrated superior performance in the recall metric, achieving a value of 96.47 compared to other methods.