There is a need for tagging of Multimedia content on the Web, specifically images, in addition, most of these images are from the medical domain continue to be neglected over the Web 3.0 and they need to be annotated and tagged in this era of Web 3.0 for easing the process of image retrieval and annotation based applications. This paper proposes a strategic model for Web Image Tagging of Medical Images using semantics and artificial intelligence techniques. The proposed framework facilitates Web Image Tagging with formalization of Term Sets by accepting the categories and annotations from the image dataset. A very strong learning infrastructure in terms of CNNs to classify the medical image dataset is encompassed, the presence of strategic domain relevant ebooks helps in curating the knowledge graph, hence furthering the auxiliary knowledge and thereby fact based reasoning. The phenomenon of ontology generation also aggregates auxiliary knowledge and populates quiz knowledge which are highly relevant to the proposed framework. The Jiang-Conrath Similarity Index along with the Normalized Information Distance and the Horn’s Index provides a very strong ecosystem for semantics similarity based reasoning and overall precision _ %, with an F-measure of _ %, lowest measure of FDR of __ has been achieved by the proposed framework for tagging of Web images pertaining to Medical domain.