The incorporation of advanced human–computer interaction (HCI) techniques into E-learning and teaching Systems has the potential to significantly enhance learning experiences through personalization. This paper offers a thorough benchmarking study focusing on cutting-edge lip-reading and face-detection algorithms, assessing their performance in optimizing these systems. We conduct a detailed analysis and comparison of the robustness and computational efficiency of various widely used deep learning-based algorithms designed for lip-reading and face detection. The insights derived from this benchmarking exercise are then utilized to pinpoint the most suitable algorithmic choices for specific E-learning and Teaching Systems scenarios. Additionally, the study highlights key challenges and outlines future research directions aimed at improving the performance and applicability of lip-reading and face-detection algorithms in the dynamic and complex environments of these systems.