Medical image classification using deep learning (DL) typically requires large and diverse datasets. However, data privacy regulations often limit data sharing across institutions. Federated Learning (FL) addresses this issue by enabling collaborative model training without transferring raw data. Despite its advantages, FL is challenged by limited data at each participating client, which can hinder model performance. To overcome this limitation, we employ Federated Transfer Learning (FTL), a hybrid approach that combines FL with Transfer Learning (TL) to improve model generalization under data scarcity. In this work, we apply FTL to chest X-ray (CXR) classification, leveraging MobileNet for one dataset and ResNet50 for another. We have evaluated our framework’s performance using various evaluation metrics. It achieved 98% accuracy and 99.97% AUC-ROC on Dataset1, and 93.46% accuracy with a 97.9% AUC-ROC on Dataset2, demonstrating its overall effectiveness. To enhance model interpretability, we use Explainable AI (XAI) techniques such as Grad-CAM and LIME to visualize decision-making. Furthermore, we employ two different GPT models-Gemini and ChatGPT-one for generating human-readable explanations based on the XAI visualizations and the other to quantitatively validate the reliability of the generated explanations on a five-point Likert scale. The proposed approach yielded reliability scores of 4.13 and 4.20 for GradCAM visualizations, and 4.43 and 4.87 for LIME visualizations, across the two datasets, indicating high reliability. Overall, the proposed FTL-XAI-GenAI framework ensures high classification performance and transparency, enabling medical professionals to understand AI-driven diagnoses while maintaining data privacy.