This paper presents our method and findings for the ML-ESG-3 shared task for categorising Environmental, Social, and Governance (ESG) impact level and duration. We introduce a comprehensive machine learning framework incorporating linguistic and semantic features to predict ESG impact levels and durations in English and French. Our methodology uses features that are derived from FastText embeddings, TF-IDF vectors, manually crafted linguistic resources, the ESG taxonomy, and aspect-based sentiment analysis (ABSA). We detail our approach, feature engineering process, model selection via grid search, and results. The best performance for this task was achieved by the Random Forest and XGBoost classifiers, with micro-F1 scores of 47.06 % and 65.44 % for English Impact level and Impact length, and 39.04 % and 54.79 % for French Impact level and Impact length respectively.