K. Manohar Rao, Hruthik Sheelam, Aravind Seelam, Ajay Erumandla 02 7(1) Botdriven cyberbullying has become a growing concern in digital communication with statistics indicating that over 59 of teenagers have encountered online harassmentmost commonly on social media platforms accounting for over 70 of reported cases Multiclass cyberbullying datasets typically contain up to 25000 labeled entries across categories such as insults threats racism and sexism often suffering from severe class imbalance and linguistic diversity Manual detection techniques are plagued by inconsistencies subjectivity and limited scalability amidst the rapidly growing volume of usergenerated content Traditional machine learning models struggle with shallow feature representations poor performance on minority classes and difficulty in detecting nuanced or implicit abuse Additionally current literature often overlooks the integration of deep ensemble learning with optimized preprocessing and contextual analysis To overcome these challenges this study introduces a Hybrid Multiclass Unmasking Bot Classification framework The proposed approach combines featureenriched Ngram extraction with a dual deep learning architecture incorporating both Deep Neural Networks DNN and Convolutional Neural Networks CNN The pipeline begins with comprehensive dataset ingestion and Exploratory Data Analysis EDA to understand class distribution and data imbalance Text preprocessing including tokenization lemmatization and noise elimination is followed by vectorization using TFIDF with bigram support enabling the capture of both isolated and contextual semantics The DNN component captures abstract semantic relationships while the CNN detects local linguistic cues This dualstream structure promotes robust learning across varying categories of cyberbullying bots Performance evaluation demonstrates that the proposed system significantly outperforms conventional classifiers in terms of precision recall and F1score across all classes Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026