In Today’s world, YouTube and other content creators are frequently barraged by a deluge of spam, unrelated remarks, and even hate messages, which makes it challenging for them to have a healthy and active online community. The more their audience, the more time-consuming are the traditional moderation practices and the less insight the audience opinion provides. This disconnection renders the creators incapable of knowing and redressing the problems of their community. Adding to the problem are the fuzzy logic limitations in identifying subtle topics and emotive cues, where misinterpretation can diminish the strength of content analysis. To fill these gaps, the NLP-driven Smart Comment Manager is set to simplify and enhance comment analysis. The platform is made user-friendly and removes hate speech, spam, and clusters duplicate questions, allowing creators to respond better and build a better and more engaging community. This system is built from three foundation modules: Hate Speech and Spam Detection, with RoBERTA to detect toxic content quickly and efficiently; Similar Question Clustering, with stsb-distilbert-base, to cluster repetitive questions for best responses; and Topic Discovery, with BERTopic, to detect trending topics in both comment streams and live chat. Smart Comment Manager converts comment streams into active feedback engines for building audience engagement and community, not passive feedback pipelines. Beyond content creation, the platform is also beneficial to customer support teams and online communities where handling massive amounts of user feedback is critical.