As a recognizing in machine learning algorithm a significant amount of in different various field has been done in many technologies field of machines through which speech has a major impact research interest, especially in the affective computing domain. Increasing potential, algorithmic advancements, and applications in real-world. This human speech contains para-linguistic information that can be represented using different various quantitative features such as pitch, intensity for its deltaic result. It is commonly achieved following three key steps: data processing, feature extraction, and classification based on the underlying emotional features. The nature of these steps, help with the distinct features of human speech, to get the exact result through the underpin with the use of ML methods. Many techniques have been utilized to extract emotions from signals, including many well-established speech analysis and classification techniques. Emotion recognition the review covers databases used, emotions extracted, contributions made toward emotion recognition and limitations related to it. signals are an important but challenging component of Human-Computer Interaction (HCI) in machine learning aspect in computer machines through various different perspective and given signals