Huntington disease is a neurodegenerative genetic condition brought on by an HTT gene mutation that produces a toxic protein that damages neurons. This progressive disease primarily affects motor function, cognition, and psychiatric health. Huntington disease detection is the need for more accurate, objective, and early diagnostic tools to predict disease progression. To overcome these challenges, a novel AML-HD model is proposed for the classification of Huntington disease. Initially, the Huntington images are preprocessed using a Contrast limited adaptive histogram equalization to enhance the image quality and remove the noise artifacts. In segmentation using Graph based segmentation used in HD brain images. Then feature extraction using a Random Forest for segmented images used to extract features. Finally, Support Vector Machine is used to classify types of HD namely Normal, Intermediate, Reduced penetrance and penetrance respectively. The effectiveness of the proposed AML-HD method using metrics like F1 score, sensitivity, accuracy, and specificity. The classification accuracy of the proposed AML-HD model was 96.54%. The proposed model enhanced the total accuracy by 3.81%, 3.71%, and 6.71% better than CNN, FPSOCNN, and ODNN respectively.