Emotion recognition can be arduous for machine learning algorithms, especially when a multitude of test samples are input from various people. A way to combat this could be the use of ensemble learning. Ensemble learning allows for a combination of multiple machine learning algorithms to come to the most accurate conclusion based upon multiple predictions. In this paper, we devise a method of emotion recognition using ensemble learning of multiple machine learning algorithms from: k-nearest neighbors (KNN), multilayer perceptron (MLP), and convolutional neural networks (CNN). A combination of these relatively accurate algorithms can establish a versatile model for emotion recognition that classifies a plethora of input data. Using ensemble learning, we were able to create a generalized and accurate model for emotion recognition. Using the collection of emotional speech recordings, following a template like the RAVDESS speech data set. Our hybrid model using ensemble learning was able to achieve accuracy ratings of up to 84.2% on the given data set.