In this work, we tackle the challenge of multi-label emotion classification, where a sentence can simultaneously express multiple emotions. This task is particularly difficult due to the overlapping nature of emotions and the limited context available in short texts. To address these challenges, we propose an ensemble approach that integrates Pre-trained Language Models (BERT-based models) and Large Language Models, each capturing distinct emotional cues within the text. The predictions from these models are aggregated through a voting mechanism, enhancing classification accuracy. Additionally, we incorporate threshold optimization and class weighting techniques to mitigate class imbalance. Our method demonstrates substantial improvements over baseline models. Our approach ranked 4th out of 90 on the English leaderboard and exhibited strong performance in English in SemEval-2025 Task 11 Track A.