In recent years, the widespread adoption of virtual and hybrid learning environments has introduced elevated risks of identity fraud, impersonation, and academic dishonesty in online classrooms. This manuscript presents a novel framework for secure identity verification in virtual classrooms by harnessing deep‐learning‐based biometric authentication techniques. The proposed system integrates facial recognition, voice verification and behavioural biometrics from the student’s interaction in the virtual platform to ensure a robust one‐to‐one user verification prior to, and during, the class session. Through this multi‐modal biometric approach, the system addresses the limitations of password‐based access and single-factor authentication. Experimental evaluation demonstrates high accuracy, low false acceptance and false rejection rates, and resilience to spoofing attacks in a simulated virtual classroom scenario. The results indicate that employing deep convolutional neural networks for face/facial landmarks, recurrent neural networks for voice dynamics, together with continuous behavioural pattern monitoring offers a practical and scalable solution for e-learning platforms. In conclusion, this work offers a pathway to strengthen trust and security in virtual education by embedding deep‐learning biometrics into identity verification workflows, enabling more reliable digital learning environments.