Emotion detection is an essential tool for gaining insights into user or customer feedback on products, whether it is through social media comments, product reviews, or news discussions. However, accurately identifying emotions in text is a challenging task due to the complexity and variability of human language. To address this challenge, machine learning and data analysis techniques are often employed. This paper presents an enhanced voting rule-based framework that systematically combines predictions from six transformer models through adaptive fusion strategies. By combining the outputs of multiple BERT models through hierarchical decision rules, this approach leverages the complementary strengths of diverse transformer architectures to enhance overall classification accuracy. The voting mechanism systematically reduces prediction errors and model-specific biases that individual models might introduce, ensuring that the final prediction is more reliable by reflecting the consensus of multiple models rather than relying on a single model. Experimental evaluation on the SemEval 2025 Task 11 dataset demonstrates our proposal has achieved Macro F1 of 0.7462 and Micro F1 of 0.7770, consistent improvements over individual transformer models (Macro F1 is up 1.22 percentage points over the best performing DeBERTa) and substantial gains over conventional ensemble approaches (9.67 percentage points over majority voting). These results demonstrate that aggregating transformer models through our efficient voting mechanism with hierarchical decision rules is a highly effective strategy for improving emotion detection accuracy in natural language processing tasks and is readily adaptable to other emotion detection methods.