This research presents a comprehensive analysis of news articles with the primary objectives of exploring the underlying structure of the data and detecting events contained within news articles. The study collects articles from Greek online newspapers and focuses on analyzing a subset of this data, related to a predefined news topic. To achieve this, a hybrid approach that combines topic modeling, feature extraction, clustering, and named entity recognition, is employed. The obtained results prove to be satisfactory, as they demonstrate the effectiveness of the proposed methodology in news event detection and extracting relevant contextual information. This research provides valuable insights for multiple parties, including news organizations, researchers, news readers, and decision-making systems, as it contributes to the fields of event detection and clustering. Moreover, it deepens the understanding of applying solutions that do not require explicit human intervention, to real-world language challenges.