Hate speech continues to be a pressing concern in online social media (OSM) platforms, necessitating effective automated detection systems. In this paper, we propose a unified approach, encompassing both Task 1 & 4, to tackle the challenge of hate speech recognition within the HASOC 2023 framework. It addresses the complexities of multilingual OSM by employing cutting-edge Natural Language Processing (NLP) techniques and leveraging powerful language models put forward by team CNLP-NITS-PP. The key objective is optimising precision-recall trade-offs in hate speech detection, spanning English and Indo-Aryan languages. The empirical results demonstrate the effectiveness of our approach in isolating explicit signs of hate speech, emphasizing model efficiency, interpretability, and the importance of diverse linguistic nuances in creating safer online environments. This integrated work sets the stage for advancements in hate-span detection and underlines the significance of fostering responsible and inclusive online conversations across various language environments.