The internet and healthcare industries rely heavily on deep learning and transfer learning. Almost every sector of the modern internet economy is dependent on face recognition software. The environment is always evolving, and so are automated face recognition (AFR) systems. With Smart Attendance with Real-Time Face Recognition, keeping track of students\' daily attendance is a breeze. One approach that has been suggested builds a dataset using images of students in class. We employ the Multitask Convolution Neural Network technique to detect and identify faces. Facial recognition will be followed by data submission. The suggested approach was contrasted with other alternatives, including the Haar Cascade Convolution Neural Network Classification method. The suggested approach outperformed the alternatives in terms of results.