A basic component of daily interactions and human behavior is the expression of emotions. Emotional recognition is a computer-based system that identifies the feelings associated with objects that are identified. Algorithms capable of detecting, extracting, and assessing these facial expressions will enable automated sentiment analysis of individuals in images and videos. Therefore, facial movements, voice, behavior, or physiological data may all be used to identify emotions. The development of affective computing can benefit from the precise identification of human emotions. The study focuses on creating a facial expression recognition model to identify and analyze an individual's emotions with the help of Convolutional Neural Network (CNN). The research methodology is efficient, shows successful performance and an accuracy of 72.79%. It provides a deeper understanding of current emotion recognition systems and can assist researchers in selecting suitable algorithms and datasets.