Speech emotion recognition (SER) is a challenging task in the field of artificial intelligence and machine learning. Over the years, researchers have proposed various approaches to recognize emotions from speech signals. This article will analyze and discuss some of the previous works on machine learning and deep learning in SER. This survey study focuses on the importance of the human voice in determining emotional and psychological states. Various methods were used to successfully classify emotions such as anger, sadness, happiness, fear, disgust, neutral, and surprise. Reviewed in this study was conducted in sequential stages including pre-processing treatment, feature selection, classification, and evaluation of results. different data sets were also reviewed for international languages such as English, Hindi, German, Urdu, Tamil, French, and Arabic. The study primarily focused on artificial intelligence and machine learning algorithms due to their flexibility and ease of understanding with distinct results.