Abstract: In this study, an AI-powered fashion recommendation system that integrates image processing and real-time weather data to offer tailored outfit recommendations is investigated. The system allows users to upload images of clothing, which are processed using convolutional neural networks (CNN) for feature extraction such as color, texture, and garment type. At the same time, weather data is accessed through the OpenWeatherMap API. A rule-based and optionally machine learning-augmented recommendation engine thereafter recommends weather-matched ensemble combinations. The solution improves the decision-making of the user by considering personal style and environmental factors, offering context-sensitive and smart wardrobe guidance. The system does not only aim at fashion-conscious users but also at fashion retailers and online stores looking to boost customer interaction through intelligent clothing recommendations based on local weather patterns and individual tastes. Integration of AI and image processing in fashion bolsters the user experience through automation of choosing an outfit and making sure that the garments match both functional and aesthetic requirements. The model supports sustainable fashion in terms of better use of current wardrobes and minimized unwanted purchases. The study also assesses the scalability and flexibility of the system for various demographics. of users and worldwide meteorological conditions, thereby creating a framework for future developments in fashion technology.
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DOI:
10.17148/IJARCCE.2025.14642
