This paper presents our approach for SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection. We investigate multiple methodologies, including fine-tuning transformer models and few-shot learning with GPT-4o-mini, incorporating Retrieval-Augmented Generation (RAG) for emotion intensity estimation. Our approach also leverages back-translation for data augmentation and threshold optimization to improve multi-label emotion classification. The experiments evaluate performance across multiple languages, including low-resource settings, with a focus on enhancing cross-lingual emotion detection.