Recently, the criticism of the emotional material in speech became a topic of extensive interest because it is used in contact with computers and mental health supervision and automatized customer service. The given paper introduces an exhaustive work of the article Echoes of Emotion: A Machine Learning Approach to Speech Sentiment Analysis, which discusses the power of machine learning based methods in identifying and categorizing emotions by using speech signals. We illustrate some of the feature extraction schemes like deep learning-based feature extraction like prosodic, spectral and representations and compare the performances on known speech emotions databases. This has been achieved through the use of state of art machine learning models, specifically support vector machines, random forests and deep neural networks, to correctly determine emotional states such as happiness, sadness, anger and neutrality. The findings indicate that machine learning models, especially those ones that employ deep learning network, attain encouraging accuracy in classification. The results lead to the possibility of speech emotion recognition systems to improve user inter- action with interactive technologies and bring insight into emotional expression using speech.