In this work, a new technology for recognizing an emotional facial expression is designed based on machine learning techniques emphasizing the Support Vector Machines (SVM) classifier. It encompasses advanced feature extraction techniques such as Facial Landmark Detection, Gabor Filters, and Histogram of Oriented Gradients (HOG) to detect and classify facial emotions effectively. Integrating these methods improves the model’s detecting and classifying subtle facial expressions’ accuracy in different conditions. From our findings, the model accuracy was up to 92%, with precision, recall, and F1-score of 0.93, 0.91, and 0.92, respectively. Such results proclaim strong effectiveness in facial expression detection, thus allowing for the use of the model in healthcare, human-computer interaction, and emotion recognition systems.