<p>Accuracy, low latency and privacy sensitive smart systems are a requirement in the diagnosis of prostate diseases due to the fact that the prevalence of the disease is steadily growing, and the origin of clinical information is diverse. However, the existing diagnostic frameworks are informed by uni-modal inputs and centralized deep learning models, which implies that they are less prepared to capture as well as embrace intricate cross-modal interactions and that they demand extra effort in the regard of scalability, privacy and real-time implementation. The research suggested to meet these drawbacks is a Quantum Transformer-Edge Multi-Mode Diagnostic Framework (QT-EMDF) to classify prostate disease automatically. The framework incorporates multi-modal data; medical imaging, clinical indicators, and patient attributes, and fuses using a transformer-based fusion architecture that is able to capture the long-range and inter-modal relationship model. Transformer attention mechanism has quantum machine learning built into its layers, allowing feature representations to have more richness, non-linear modeling, and reduced complexity of their parameters. In a bid to facilitate real-time clinical use, the proposed model is streamlined to run on the edge using lightweight architectural design and adaptive compression so that it has low latency and privacy-preserving inference through reduced transmission of raw data. A large-scale of experimental analyses of benchmark prostate disease datasets indicate that QT-EMDF has always been superior to the traditional convolutional networks, recurring models, and classical transformers with respect to classification accuracy, sensitivity, specificity, and F1-score. In addition, the quantum enhanced attention mechanism may assist in converging at higher rates and operate more effectively with smaller datasets, whereas edge-based execution may assist in reducing the inference time and the communication cost by a significant margin.</p>