Presenting the network architecture EMOTE-Net is a method of enhancing the face emotion recognition and classification in video data for this work. The suggested model merges the use of DenseNet to extract features with the SVM (support vector machine) to categorize the data by specifying SVM here. This feature of EMOTE-Net is highly outstanding because SVM and DenseNet are combined and are thus capable of sophisticated classification and effective feature extraction. The first process to come in methodology is preprocessing of video data. Bounding Box detection is able to extract regions that are of interests (ROIs) and that Densenet is great at the feature representation with high dimensions. Henceforth, feed these features into a classifier from SVM for intelligent categorization. Evaluation has provided clear evidence regarding the efficiency of this model, which has obtained the accuracy of 0.9890, precision of 0.9900, sensitivity of 0.9877, specificity of 0.9972, and F1 score of 0.9886. The pertinence of EMOTE-Net to real life applications, such as video analytics, human-computer interaction, and surveillance, will be highlighted in the chapter through the references from the installation and evaluation processes. The work presents a viable approach for object detection and classification in changeful visual arenas.