Guava is a widely cultivated fruit crop, but is hindered by leaf diseases such as canker, dot, and rust, which demand labour-intensive manual detection. This study presents a deep learning-based system for automated guava leaf disease diagnosis, employing a hybrid model combining EfficientNetV2 and Vision Transformers (ViT) to achieve high accuracy and interpretability. The dataset comprises five classes (canker, dot, mummification, rust, and healthy). Explainable AI, specifically Grad-CAM, was integrated to visualize critical image regions to enhance transparency. The model, trained on 80% of the dataset and tested on the original images. The model achieved 95% accuracy in disease classification. According to the detected disease, recommendations are provided that include treatment options and required preventive measures. Deployed as a web-based application, this system delivers an accessible, real-time solution for guava health management, highlighting the potential of explainable deep learning in agriculture.