The increasing demand for personalized skincare has propelled the need for intelligent, data-driven solutions that address individual skin concerns with precision. Traditional skincare recommendations often lack adaptability and fail to consider the unique characteristics of a user’s skin, such as tone, texture, and condition. This research presents an AI-driven framework that leverages computer vision and deep learning to analyze facial images and identify key dermatological features including skin type, pigmentation, acne presence, and dryness indicators. The proposed system incorporates a convolutional neural network (CNN) for skin feature extraction and classification, followed by a recommendation engine that suggests suitable skincare products aligned with the user's skin profile. Real-world datasets comprising diverse skin types and conditions were used to train and validate the model, achieving high classification accuracy and robust generalization across varying lighting and ethnic contexts. The integration of domain-specific dermatological rules with machine learning outputs ensures both reliability and personalization. Experimental results demonstrate the effectiveness of the approach in delivering tailored product suggestions that significantly improve user satisfaction compared to generic alternatives. This study contributes to the advancement of AI applications in digital dermatology and presents a scalable solution for personalized skincare services. Future work includes expanding the dataset and integrating IoT-based skin sensors for real-time analysis and recommendation.

Keywords: Artificial Intelligence, Facial Feature Extraction, Skin Tone Analysis, Personalized Skincare, Product Recommendation System, Deep Learning