The study suggests a novel strategy to improve hate speech detection’s efficacy and efficiency. The authors present a model based on Support Vector Machine (SVM) methods and examine the drawbacks of existing approaches. When it comes to hate speech detection, our comparison investigation shows that SVM-based models perform better than neural network approaches. This is explained by the SVM’s efficiency in classifying intricate, nuanced patterns and its capacity to handle high-dimensional data. Examining the ethical and technological ramifications of automating the identification of hate speech, the study explores how to strike a balance between algorithmic bias, model interpretability, and the necessity for constant adaptation to changing societal norms and language. It also considers how to combine accuracy with ethical issues like censorship and free speech.