Topic analysis within broad and evolving fields poses great challenges when attempting to be addressed by traditional methods. In response, topic modeling seeks to automate the identification and analysis of underlying themes within several collections of documents in order to synthesize and facilitate the interpretation of their content. The present study applied multiple iterations of the Latent Dirichlet Allocation (LDA) model and the Best-K mechanism in the identification of topics within a volume of 250 news items labeled with “Artificial Intelligence” within a mainstream web portal. Additionally, a large language model (LLM) was implemented to improve interpretation and description for the labeling of found topics. As a result, 9 key topics were identified ranging from the main trends and challenges in AI.