Diabetic foot ulcer (DFU) is a potentially fatal complication of diabetes. Traditional techniques of DFU analysis and therapy are more time-consuming and costly. Artificial intelligence (AI), particularly deep neural networks, has demonstrated remarkable effectiveness in medical applications. Despite this, the lack of explainability of deep learning models is currently viewed as a key hurdle to using these approaches in actual clinical settings. In this research, we present the DFU_XAI framework for assessing the interpretability of explainable-driven deep learning (DL) models. DFU_XAI evaluates five DL models (Xception, DenseNet121, ResNet50, InceptionV3, and MobileNetV2) to establish a transparent DL framework using three state-of-the-art explanation methods: Shapley additive explanation (SHAP), local interpretable model-agnostic explanations (LIME), and gradient-weighted class activation mapping (Grad-CAM). ResNet50 outperformed the other four models with remarkable results: 98.75% accuracy, 99.2% precision, 97.6% recall, 98.4% F1-score, and 98.5% AUC. For the most part, it can locate diabetic foot ulcers precisely on a diabetic foot and discriminate between diabetic foot ulcers and healthy feet in the DFU dataset. A heat map will indicate the precise location of the ulcer that needs care.