Diagnosing gastrointestinal (GI) disease from endoscopic images remains a challenging task due to high intra-class variability, inter-class similarity, and limited annotated data. To address these issues, this study proposes a novel Dynamic Few-Shot Learning (DFSL) framework that integrates the representational strength of convolutional backbones with a dynamic metric-based classifier to perform accurate classification under low-data regimes. We benchmark our approach using DenseNet-201, ResNet-50, and ViT-B/16 across multiple few-shot settings (1 to 5 shots, up to 4-way classification). Among them, DFSL with ResNet-50 consistently outperforms state-of-the-art backbones, achieving a peak accuracy of 97.6% in the 4-shot scenario. Our experiments reveal that ResNet-50's residual connections enable more robust spatial encoding and generalization across variations in the number of shots. To further enhance interpretability and clinical relevance, Grad-CAM and TCAV were employed to visualize the decision rationale and validate the model’s focus on disease-specific regions. A comparative analysis with supplementary confusion matrices reveals that DFSL-ResNet-50 achieves superior class-wise performance, with reduced misclassification of visually similar diseases. While ViT-B/16 and DenseNet-201 exhibit fluctuating accuracy under scarce data, our model maintains consistent performance. The proposed DFSL framework, with its potential for clinical deployment, presents a significant advancement in developing scalable, interpretable, and accurate diagnostic systems, instilling optimism for its real-world application.