The proposed model employs advanced facial recognition technology and machine learning algorithms to assess students' focus levels during the learning process. By analyzing facial expressions such as happiness, neutrality, surprise, sadness, fear, disgust, and anger, the model aims to infer students' understanding of lessons in offline and online settings. Training on a diverse dataset ensures its accuracy and applicability. The study's goal is to enhance teaching and learning outcomes by providing valuable insights to teachers in offline classrooms, identifying students who need additional support. In online settings, educators can gauge students' engagement and implement strategies to improve focus and learning experiences. This research bridges the gap between offline and online education, revolutionizing personalized learning and promoting student success.