Diseases can show facial characteristics and deformities, which can serve as diagnostic markers for various conditions like endocrine and metabolic syndromes, genetic disorders, and facial neuromuscular diseases. While facial recognition technology has been around for over fifty years, its use in clinical medicine has surged in the past decade. AI-based facial recognition has shown superior performance in diagnosing diseases. The research aims to establish a framework and methodology for identifying optimal algorithms and techniques for early diagnosis of neurodegenerative diseases. Through a comprehensive review and modeling of existing algorithms in the literature. The proposed development focuses on the early detection of neurodegenerative diseases to facilitate timely treatment and disease management. The implementation of the machine learning algorithms which are derived of convolutional neural networks (CNN), histogram of gradients (HOG), OpenFace 2.0 and supporting vector machine (SVM) in the proof-of-concept simulation achieve an accuracy of 87,5% and an F1 Score 83,3%. These results and its approach show a superior risk detection accuracy than that of the human physicians and thus is a viable diagnostic support for identifying neurodegenerative illness and referring patients to the appropriate medical specialist.