Social media has transformed into a powerful tool for sharing information while upholding the principle of free expression. However, this open platform has given rise to significant issues like hate speech, cyberbullying, aggression, and offensive language, negatively impacting societal well-being. These problems can even lead to severe consequences such as suicidal thoughts, affecting the mental health of the victims. Our primary goal is to develop an automated system for the rapid detection of offensive content on social media, facilitating timely interventions and moderation. This research employs various machine learning classifiers, utilizing character N-gram TF-IDF features. Additionally, we introduce SVM, RL, and Convolutional Neural Network (CNN) models specifically designed for hate speech detection. SVM utilizes character Ngram TF-IDF features, while CNN employs word embedding features. Through extensive experiments, we achieved optimal results, with a weighted F1-score of 0.77 in identifying hate speech and offensive language.