Facial Expression Recognition (FER), is necessary for the connection between humans and computers and affective computing, it has evolved significantly in recent years, with both Machine Learning-Based Face Expression Recognition Systems (ML-FERS) and Deep Learning-Based Face Expression Recognition Systems (DL-FERS) playing pivotal roles. This paper presents a comprehensive survey that explores the latest developments in Facial Expression Recognition Systems (FERS), encompassing both deep learning-based and traditional machine learning-based approaches. It provides an elaborate examination of the critical components of FERS, including data acquisition and preprocessing, feature extraction, emotion modeling, and evaluation metrics. By analyzing the strengths and limitations of these techniques, the survey offers insights into their applicability across different domains and practical scenarios and provides a valuable resource for researchers, practitioners, and decision-makers, facilitating informed decision-making and responsible development in this dynamic and influential area of technology.