Abstract—The paper aimed to analyze the thematic evolution and various networks of intellectual structures in the field of biomedical ontologies during 2014-2023. This applied research used an analytical and descriptive method, co-word techniques, and social network analysis. A web-based interface of bibliometrix, Microsoft Excel, and VOSviewer software were used for descriptive bibliometric study, data analysis, and network structure visualization. The period from mid-2020 to mid-2021 presented an increased dissemination of significant and prominent keywords within the overlay network in the field. Five major topic clusters were identified based on a co-occurrence network. These clusters labeled ‘gene ontology’, ‘biomedical informatics focusing on AI techniques’, ‘bioinformatics applications in biomarker discovery’, ‘protein interaction networks in Alzheimer's proteomics’, and ‘network-based molecular mechanism’. Basic clusters were ’gene ontology’, ‘bioinformatics’, and ‘gene expression’. Moreover, five clusters experienced significant developments between 2023 and 2024, namely ‘bioinformatics’, ‘deep learning’, ‘machine learning’, ‘transcriptome’, and ‘network pharmacology’. These topics are the latest and hottest concepts in this field. Clusters, namely ‘deep learning’,’ machine learning, and ‘ontology’ were recognized as niche and the most well-developed themes. The most mature and mainstream thematic clusters were namely ‘transcriptome’, ’prognosis’, and ‘rna-seq’. The most undeveloped and chaotic themes were ‘network pharmacology’ and ‘molecular docking’.