Vascular dementia (VaD) presents significant diagnostic challenges due to its heterogeneous clinical manifestations and the overlap of symptoms with other neurological disorders. Traditional diagnostic approaches rely heavily on expert interpretation of MRI scans, a process that is often time-consuming, subjective, and susceptible to inter-observer variability. To address these limitations, an AI-powered web application has been developed to facilitate rapid, accurate, and interpretable diagnosis of vascular dementia using brain MRI data. This system employs advanced deep learning architectures, including VGG16 and DenseNet121, to perform both binary classification (distinguishing VaD-demented from non-demented cases) and multi-class subclassification into four VaD subtypes: Binswanger, Hemorrhagic, Strategic, and Subcortical dementia. The prototype demonstrated a testing accuracy of 96% for binary classification and 88.25% for subtype analysis. A distinctive feature of the system is the integration of explainable AI techniques, such as Grad-CAM and LIME, which provide visual and textual explanations to support clinical decision-making and foster trust in AI-driven outcomes. The web-based interface supports DICOM, JPEG, and PNG formats, enabling clinicians to efficiently upload MRI scans, receive diagnostic predictions, and download comprehensive reports. By combining high diagnostic performance with transparency and user accessibility, this prototype aims to bridge the gap between artificial intelligence research and clinical practice, offering healthcare professionals a valuable tool for timely and reliable vascular dementia diagnosis and management