Abstract—Medical imaging in early disease identification is still a foundation in effective clinical practice. Conventional deep learning models are very powerful but they have serious challenges including high computational costs and decision making processes that are not transparent and also cannot be applied in real-time healthcare settings. These limitations prevent their implementation in the resource-constrained medical centers where diagnostics are required the most and immediately. In this paper, we introduce an Edge-Optimized Explainable Deep Learning Framework which is intended specifically to diagnose multiple diseases based on medical images. The proposed method is a mixture of computationally efficient Convolutional Neural Networks and Explainable Artificial Intelligence methods, which form a system that does not only provide accurate predictions but also provides sensible explanations to the clinical decisions. The framework is efficient on the edge computing devices, and the processing delay is low, the energy use is low, and the diagnostic performance is consistent across many medical cases. The time taken to process an image dropped by a factor of 20–40 compared to current methodologies to 1.1 seconds. Other measures prove the reliability of the system: 95.7 percent precision, 95.9 percent recall, 95.8 percent F1-score, and 0.983 Area Under Curve. These findings validate the fact that our framework is ready to be deployed in time sensitive and resource constrained healthcare settings that demand accuracy and transparency. Index Terms—Deep Learning, Edge Computing, Explainable Artificial Intelligence, Multi-Disease Detection, Convolutional Neural Networks