In response to the growing challenge of propaganda through online media in online news, the increasing need for automated systems that can identify and classify narrative structures in multiple languages is evident. We present our approach to the SemEval-2025 Task 10 Subtask 2, focusing on the challenge of hierarchical multi-label, multi-class classification in multilingual news articles. Working with a two-level taxonomy of narratives and subnarratives in the Ukraine-Russia War and Climate Change domain, we present methods to handle long articles based on how they are naturally structured in the dataset, propose a hierarchical classification MLP with respect to the narrative taxonomy structure, and establish a continual learning training strategy that takes into advantage the multilingual nature of our data and tries to examine how different language orders affect performance. Our final system was evaluated in five languages, achieving competitive results while demonstrating low variance compared to similar systems in our leaderboard position.