Abstract: The skincare industry sees rising demand for personalized solutions for different skin types and preferences. Our research introduces an innovative method employing Convolutional Neural Networks (CNN) in the "Intelligent facial skin care recommendation webapp" to classify skin types and offer personalized skincare advice. This webapp merges advanced machine learning with a user-friendly interface, allowing users to upload images for skin analysis. The CNN model accurately categorizes skin types like dry, normal, sensitive, oily, scaly, skin moles, and red spots. Our methodology involves comprehensive data collection from user profiles, skin attributes, and product information. Data preprocessing extracts relevant features, ensuring model accuracy. The webapp's implementation includes a seamless frontend for user interaction and a robust backend for real-time data processing and recommendations. Users get personalized skincare advice based on skin type, preferences, and feedback. Our research's implications extend to revolutionizing skincare routines. Future enhancements may include user feedback loops, AI-driven chatbots for consultations, and expanding the product database. Our work advances personalized skincare solutions, showcasing machine learning's transformative impact in skincare.