International Journal of Research and Development in Engineering Sciences https://www.ijrdes.com Kondala Geethanjali 3 5 (September - October) 2021 10 September-October enMachine learningToxic comment classificationNLPDeep learningOnline moderation With the rapid expansion of social media and online communication platforms the proliferation of toxic comments such as hate speech harassment and offensive languagehas become a significant concern Detecting and moderating such content is crucial for maintaining healthy digital interactions This paper presents an overview of machine learning ML methods used for online toxic comment classification Various supervised and deep learning approaches including Logistic Regression Support Vector Machines SVM Random Forests Recurrent Neural Networks RNN and Convolutional Neural Networks CNN are analyzed for their performance in identifying toxic behavior in textual data The study emphasizes the importance of data preprocessing feature extraction techniques such as TFIDF and word embeddings and model optimization for improving classification accuracy It also highlights recent advancements in transformerbased models like BERT which achieve superior context understanding and robustness Challenges such as data imbalance context ambiguity and evolving language patterns are discussed The paper concludes that integrating traditional ML with deep learning and Natural Language Processing NLP techniques offers a powerful solution for effective and scalable toxic comment detection Copyright (c) 2026 International Journal of Research and Development in Engineering Sciences. All rights reserved. 2021 All Rights Reserved