Diabetic foot ulcers (DFUs) represent a major health issue in India, affecting a significant portion of diabetic patients. If not promptly addressed, these ulcers can lead to severe complications such as infection, gangrene, amputation, and chronic wounds. With 72% of DFU patients testing positive for multidrug-resistant organisms (MDROs), the risk of complications is notably elevated. Early detection is crucial for preventing these severe outcomes. This prototype employs artificial intelligence (AI) and deep learning techniques, specifically Convolutional Neural Networks (CNNs), to detect and assess DFUs. By analysing annotated medical images, the system precisely measures the size and depth of ulcers using CNNs. AI facilitates early diagnosis, allowing for timely and tailored treatments, which enhances clinical decision-making and reduces the risks associated with advanced DFUs. The system utilizes an ESP32 camera to capture real-time images of the ulcers. Following image capture, the CNN algorithm performs image masking to isolate the ulcer region. The contours of the wound are displayed on a terminal, and the severity percentage of the ulcer is calculated along with recommended interventions based on the wound&'s stage. This approach not only reduces healthcare costs but also improves patient outcomes by preventing severe complications. The study underscores the importance of early diagnosis and highlights AI's potential in effective DFU management.