This study introduces “Duration Dynamics: Fin-Turbo’s Rapid Route to ESG Impact Insight”, an innovative approach employing advanced Natural Language Processing (NLP) techniques to assess the impact duration of ESG events on corporations. Leveraging a unique dataset comprising multilingual news articles, the research explores the utility of machine translation for language uniformity, text segmentation for contextual understanding, data augmentation for dataset balance, and an ensemble learning method integrating models like ESG-BERT, RoBERTa, DeBERTa, and Flan-T5 for nuanced analysis. Yielding excellent results, our research showcases the potential of using language models to improve ESG-oriented decision-making, contributing valuable insights to the FinNLP community.