Diabetic foot ulcers (DFUs) pose a significant healthcare challenge due to their prevalence and potential complications. Early and accurate detection of DFUs is crucial for timely intervention and prevention of severe complications. It is study proposes an innovative approach for automated DFU detection utilizing Convolutional Neural Networks (CNNs), a powerful class of deep learning algorithms widely recognized for their proficiency in image analysis tasks. The proposed CNN model is trained on a comprehensive dataset of foot images, encompassing a diverse range of DFU types, stages, and conditions. The training process involves learning intricate patterns and features indicative of DFUs, enabling the model to generalize well to unseen data. The CNN algorithm's effectiveness in feature extraction and spatial hierarchy learning is harnessed to identify subtle visual cues associated with DFUs, enhancing diagnostic accuracy. The proposed system is designed to operate on medical images, particularly those obtained through various imaging modalities such as digital photography or thermal imaging. Through rigorous validation and performance evaluation, the CNN model exhibits promising results, showcasing its potential as a reliable tool for automated DFU detection. The integration of it is a technology into clinical practice holds the promise of expediting the diagnostic process, facilitating timely medical interventions, and ultimately improving patient outcomes. It is a research contributes to the ongoing efforts in leveraging advanced technologies to address critical healthcare challenges, particularly in the realm of diabetic care and wound management