Face physiognomy, the ancient practice of analyzing facial features to infer personality traits and fate, has traditionally been subjective and inconsistent. In this paper, we present a rule-based artificial intelligence system for automating facial physiognomy analysis. Our approach utilizes computer vision techniques, OpenCV, and facial landmark detection to extract and quantify key facial attributes such as face shape, eye, mouth, and nose structure. By applying predefined rule-based physiognomic principles, our system systematically classifies facial characteristics with minimal human intervention. Initial experiments show promising results, particularly for attributes that can be quantitatively measured, demonstrating the feasibility and effectiveness of our approach. The application of AI in physiognomy not only enhances the consistency and accuracy of facial feature interpretation but also makes face reading more accessible to the general public. By digitizing traditional physiognomic knowledge, AI-driven systems can provide instant, objective assessments that were previously reliant on subjective human expertise. This advancement opens new opportunities for integrating physiognomy into various domains, such as personality analysis, career guidance, and psychological research. This work provides a structured and computational perspective on face physiognomy, bridging traditional practices with modern AI-driven automation and promoting wider adoption through digital solutions.