In today's educational system, maintaining students' interest is of the utmost importance. Researchers have been motivated to investigate approaches that can automatically measure engagement as a result of this. Using facial expressions to determine whether or not a student is interested in a certain topic and to give individualized assistance is the purpose of this research project. Using the well-known VGG16 model, This study provide a framework for evaluating degrees of engagement based on facial signals. This framework is built on face detection which used the FER2013 dataset, produced encouraging findings with a considerable accuracy of 96.16%. The objective of this study is to provide educators and online platforms with a dependable instrument that can assess and address student involvement. This will be accomplished by using deep learning algorithms that are particularly intended to do facial expression interpretation. The findings of this study provide a contribution to the growing subject of using technology to enhance learning settings by highlighting the benefits of automated systems in terms of increasing student engagement and academic accomplishment.