In contemporary society, artificial intelligence (AI) can be considered an integral part of creating and utilizing dictionaries. The paper deals with the vast impact of AI, particularly natural language processing (NLP) and machine learning (ML), in ameliorating the organization and functionality of dictionaries. It is feasible to comprehend meanings of words across diverse domains with the help of dictionaries’ effective extraction of semantic data from large textual corpora through the utilization of NLP algorithms. Furthermore, ML algorithms make operations more straightforward due to word sense disambiguation (WSD) and semantic similarity comparison, hence improving the accuracy and relevance of the facts embodied in dictionaries. The article elucidates novelty of AI lexicography in employing knowledge representation tools, such as knowledge graphs and semantic embeddings, in order to provide users with textually contextual and comprehensive facts. It also addresses the significance of semantic disambiguation and contextual analysis in interdisciplinary polysemous dictionaries which substantiates possibility of AI to illustrate complicated and ambiguous word senses among terms belonging to various disciplines. With applications and case studies in fields like biomedicine research and environmental science it is justified in the paper that AI-powered dictionaries make knowledge discovery, cross-disciplinary communication, and data interpretation possible. Moreover, the paper identifies AI lexicography problems like biases, scalability, and the dynamic nature of language, and provides future directions to tackle these problems to make dictionaries more versatile and meaningful. Overall, AI-based dictionaries are a new shift in lexicography, setting the trend for future interdisciplinary research and information dissemination.