In this study, we delve into the fascinating realm of emojis and their impact on identifying hate speech in both Bengali and English languages. Through extensive exploration of various techniques, particularly the integration of Multilingual BERT (MBert) and Emoji2Vec embeddings, we strive to shed light on the immense potential of emojis in this detection process. By meticulously comparing these advanced models with conventional approaches, we uncover the intricate contextual cues that emojis bring to the table. Ultimately, our discoveries underscore the invaluable role of emojis in hate speech detection, thereby providing valuable insights for the creation of resilient and context-aware systems to combat online toxicity. Our findings showcase the potential of emojis as valuable assets rather than mere embellishments in the realm of hate speech detection. By leveraging the combined strength of MBert and Emoji2Vec, our models exhibit enhanced capabilities in deciphering the emotional subtleties often intertwined with hate speech expressions.