This involves developing advanced automatic detection systems so that existing online communities, so far insulated from it by distance and difficulty of access, where much (but not all) extreme speech continues to remain outside of digitally networked discussion (not-mediated debate not necessarily because its hate status has gone uncontested or unseen within those it directly threatens). In this work, we present a robust approach that combines LSTM, a sequential model capable of handling sequential text input with the machine learning models Random Forest and Support Vector Machine (SVM) which are very commonly studied. We categorize three types of social media content—hate speech, offensive language, and neutral texts. We use the SMOTE technique to get a balanced dataset and then extract features using TF-IDF. The LSTM model was better than the baselines in capturing subtle patterns of hate speech, achieving an accuracy of 89.7% and F1-Score as high as 0.94. This performance indicates the efficacy of deep learning in modeling nuanced linguistic dynamics and context. In the future, transformer models such as BERT will be further incorporated into the studies for more advanced contextual analysis and semantic understanding to achieve maximum accuracy. This paper provides valuable information to enhance the capabilities of automated moderation for safer online environments through its hate speech detection mechanisms.