Facial expression recognition using machine learning and computer vision has become a common practice. Deep learning algorithms are trained on large datasets of labelled facial images, enabling them to identify patterns associated with specific emotions. This has proven effective for facial feature detection and emotion recognition. The rapid advancement of artificial intelligence and machine learning has greatly improved the accuracy of identifying human emotions. Facial recognition technology has emerged as a powerful tool in computer vision, allowing for the identification and analysis of individuals based on their unique facial features. It has applications in attendance tracking, identity validation at ATMs, access control, and even in enabling robots to convey emotions through facial expressions. Additionally, it plays a role in mental illness diagnosis and facilitating human psychological interactions. Algorithms like Face Net, LBPH, SVM, and CNNs are commonly used for image classification and recognition due to their high accuracy. CNNs, in particular, follow a hierarchical model that processes input through interconnected neurons, leading to accurate face expression and recognition results. Overall, facial recognition technology has seen significant advancements thanks to AI and machine learning. It offers a wide range of applications and benefits, but also raises concerns regarding privacy and ethics. With ongoing research and development, facial recognition technology continues to evolve and contribute to various industries.