It’s essential to acknowledge and validate the full range of human emotions and experiences, including the negative ones, while maintaining healthy positivity. Positive and optimistic comments and posts on social media unintentionally reinforce negative narratives of emotional suppression and invalidation. This paper concentrates on the crucial task of detecting toxic positivity, tackling the difficulties in pinpointing situations where the expression of positive sentiments overshadows the recognition of negative emotions. The proposed approach leverages advanced natural language processing techniques, employing BERT-based models and ensemble models, and achieved significant success in the detection task. The RoBERTa model yields a macro F1 score of 0.75 and a weighted F1 score of 0.86. Additionally, the ensemble model, integrating various classifiers, demonstrated a macro F1 score of 0.73 and a weighted F1 score of 0.85.