In this paper, we address the challenges of text-based emotion detection, focusing on multi-label classification, emotion intensity prediction, and cross-lingual emotion detection across various languages. We explore the use of advanced machine learning models, particularly transformers, in three tracks: emotion detection, emotion intensity prediction, and cross-lingual emotion detection. Our approach utilizes pre-trained transformer models, such as Gemini, DeBERTa, M-BERT, and M-DistilBERT, combined with techniques like majority voting and average ensemble voting (AEV) to enhance performance. We also incorporate multilingual strategies and prompt engineering to effectively handle the complexities of emotion detection across diverse linguistic and cultural contexts. Our findings demonstrate the success of ensemble methods and multilingual models in improving the accuracy and generalization of emotion detection, particularly for low-resource languages.