There is a need for ontology generation and synthesis, for highly important and essential but rare domains like sports medicine and athletics. In order to model a strategic Web 3.0. This paper proposes an ontology synthesis model which is automatic in nature for sports medicine and athletics as a domain of choice in which strategic database terms are extracted through the TFIDF and enhanced using topic modelling, fed through the wiki data pipeline to yield and enrich entities. This is to generate metatags which is subject to classification using the LSTM classifier. Subsequently, the data itself is classified using the generative adverse media networks to yield of the classified instances. The model encompasses hierarchical clustering to cluster the entities that come out of two distinct deep learning classifiers and is formalized in ontology using the normalized pointwise mutual information measure. The ontology is completed through axiomatization, reasoning, instantiation through an agent based cluster. An overall precision of 96.09 percentage with an F-measure of 97.44% and an accuracy of 97.45 percentage has been achieved by the proposed framework for ontology synthesis and the best in class and the first of its kind ontology synthesis model for sports medicine and athletics as a strategic domain.