Legal document segmentation is a critical task in the field of natural language processing (NLP), enabling efficient analysis, retrieval, and understanding of legal content. Despite its importance, research in this area for European Portuguese has been limited. To address this gap, we present a novel approach to automate the segmentation of legal judgments from the Portuguese Supreme Court of Justice into distinct sections. Leveraging a Bi-LSTM-CRF model, we developed a dataset and achieved significant results, including an accuracy of 0.9997, precision of 0.9986, recall of 0.996, and F1-Score of 0.9973. Our methodology and experimental results demonstrate the effectiveness and potential applications of our approach for the European Portuguese language.