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Facial diseases are common conditions that may develop day by day. It may appear due to several factors, including genetics, nerve neuritis, and environmental influences, such as pollution and the sun exposure. Changes in hormone levels, use of inappropriate skin products, stress and life pressures, and nutrient deficiencies can also be other causes. Hence, early diagnosis of these conditions is essential to prevent further complications. Using a computer vision system based on artificial intelligence (AI) algorithms to determine gender, origins, freckles, and Bell's paralysis is of great importance in early diagnosis and providing sufficient information about the patient's condition in real-time, thus reducing the time for diagnosis. Five artificial intelligence algorithms will be evaluated the performance in this study which is three algorithms based on machine learning using K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Machine(SVM). And two algorithms for deep learning namely Visual Geometry Group-(VGG16) and Vision Transformer-(ViT), with 20 epochs training. The results of the same dataset show that ViT has the highest overall performance among other algorithms used in this study, achieving impressive accuracy rates of 99.4% for gender recognition, 97.2% for origins classification, 99.5% for Bell's paralysis detection, and 96.8% for freckles detection. [ABSTRACT FROM AUTHOR]