This research uses deep learning and emotion analysis to detect social media hate speech better. The outcome is a reliable, widely usable system. The first step involves editing raw social media content. Machine learning models employ feature vectors derived using TF-IDF. The algorithm predicts sentiment scores and adds them to create a sentiment profile for grouping. Tuning hyperparameters improve model performance and detect harmful compounds. A weighted feature vector machine learning model improves sentiment-based classification. A loss function and gradient descent improves the model. Deep learning models boost identification accuracy, helping the system manage complicated real-time social media data. The proposed method outperforms NLP, supervised machine learning, and rule-based systems in accuracy, precision, and memory. The system can manage massive volumes of social media data in real time and is difficult to hack as user numbers expand. You can trust Indian internet laws and safety. This technology helps us identify bogus news and hate speech, improving the internet while adhering to the law and morality.