Online hate speech is a problem that has grown powerful and it has even demanded the use of automated and context-aware moderation strategies. The manual filtering is an impossibility taking into account the enormous amount of online content, therefore the intelligent systems for detection and mitigation are the must. This paper, on the one hand, indicates a very up-to-date Natural Language Processing (NLP)-based framework that not only detects hate speech but also provides contextually suitable answers to lessen its influence. On the other hand, unlike the traditional keyword-based methods that usually get bias suffers, have a shallow contextual understanding, and very high false-positive rates, the new system exploits various datasets and fine-tuned classification models to improve the level of precision and contextualization. Multi-platform data collection using APIs, headless browsers, and parallel web scraping together with the existing hate speech detection APIs such as TF-IDF and Better Profanity through a Beautiful Soup-based pipeline enables comprehensive data acquisition. The framework achieves superior precision in the detection of subtle hate expressions and plays a big part in the process of creating a more productive and safer online communication space.