This paper presents our approach to SemEval-2025 Task 11 (Track A): Bridging the Gap in Text-Based Emotion Detection, with a focus on multi-label emotion classification for the English dataset. Our methodology leverages an ensemble of transformer-based models, incorporating full fine-tuning along with additional classification layers to enhance predictive performance. Through extensive experimentation, we demonstrate that fine-tuning significantly improves emotion classification accuracy compared to baseline models. Furthermore, we provide an in-depth analysis of the dataset, highlighting key patterns and challenges. The study also evaluates the impact of ensemble modeling on performance, demonstrating its effectiveness in capturing nuanced emotional expressions. Finally, we outline potential directions for further refinement and domain-specific adaptations to enhance model robustness.