This research presents a YouTube Comments Analyzer that leverages machine learning and deep learning algorithms to examine and classify user comments. A large volume of comments is processed by the system, enabling it to detect key patterns, including sentiment classification and emotion detection. Using natural language processing and machine learning techniques, the tool provides meaningful insights to content creators for understanding their audience and to moderators for identifying problematic content. Researchers can also benefit by studying online commentary at scale. Our team collected video comments from various genres to train and develop the models, followed by evaluation using multiple performance metrics. The analysis tool achieves 96% accuracy in sentiment detection and 90% accuracy in emotion detection, successfully identifying complex patterns that manual evaluation often misses. To demonstrate the practical applicability of our models, we further developed a web-based application that integrates the analysis pipeline, providing an accessible platform for real-time comment analysis. This research highlights the effectiveness of automated text analysis in social media environments and demonstrates real-world applications for YouTube content management and audience engagement strategies.