Facial landmark detection plays a pivotal role in computer vision applications such as facial recognition, emotion analysis, and augmented reality. This study proposes an improved strategy for detecting landmarks from facial images using efficient machine learning algorithms. By leveraging Histogram of Oriented Gradients (HOG) for feature extraction and Support Vector Machines (SVM) for classification, the proposed methodology demonstrates enhanced accuracy and computational efficiency. Extensive experiments conducted on the iBUG-300W dataset show a significant improvement over existing methods. The results highlight the suitability of the approach for real-time applications while maintaining robustness to variations in pose, expression, and illumination.