The last decade has seen a steep rise in the use and dependence of society on social media. The need for detection and prevention of hate and offensive speech is more than ever. The everchanging form of natural language makes the detection of hate speech challenging, involving code-mixed text. The task becomes even more daunting in a country like India, where different languages and dialects are spoken across the country. This paper details the Code Fellas team’s approaches in the context of HASOC 2023 - Task 4: Annihilate Hate, an initiative aimed at extending hate speech detection to Bengali, Bodo, and Assamese languages. Here we describe our approaches which broadly involve Long Short Term Memory (LSTM) coupled with Convolutional Neural Networks (CNN) and pre-trained Bidirectional Encoder Representations from Transformers (BERT) based models like IndicBERT [1] and MuRIL [2]. Notably, our results showcase the effectiveness of these approaches, with IndicBERT achieving a remarkable F1 score of 69.726% for Assamese, MuRIL achieving 71.955% for Bengali, and a BiLSTM model enhanced with an additional Dense Layer attaining an impressive 83.513% for Bodo.