This paper proposes the development of an intelligent wardrobe system for clothing management, detection, and recommendation, as well as image processingbased virtual fit-on. The system utilizes machine learning algorithms and neural network based techniques to detect and identify the type, color, and pattern of clothes in the wardrobe. The intelligent system also keeps track of the user's clothing preferences and their daily routine to recommend appropriate outfits for different occasions and weather conditions. Moreover, the system uses image processing and neural network based techniques to create virtual fit on dress models where users can virtually try on different clothing items and see how they look before making a decision of what to wear. The proposed system incorporates IoT sensors and APIs to monitor the wardrobe's status, including temperature, humidity, and lighting, to maintain the quality of the garments. The system's evaluation shows that it can accurately detect, recommend clothes to users, and simulate virtual fit-on dress models, making it a promising solution for modern cloth management.