As user-created content on social media platforms like YouTube reaches a large scale, the issue of toxic comments has become a critical concern to the digital communication process and the overall mental health of the user. Traditional methods of moderation such as manual or keyword-based do not meet the volume and linguistic complexity of an online conversation. The current models also lead to this issue by using simplistic binary classification systems that cannot reflect the overlap and complexity of internet toxicity. To resolve these shortcomings, this paper introduces a contextualized Context-Aware Toxic Comment Analyzer that is trained on a fine-tuned BERT-based bidirectional transformer architecture. The framework, unlike the previous ones, uses semantic tokenization to obtain contextually rich word representations that can be accurately classified into six categories of toxicity: Toxic, Severe Toxic, Obscene, Threat, Insult and Identity Hate. It is trained and tested using a dataset of around 160,000 YouTube comments, and it contains an extensive text preprocessing pipeline and word cloud-based analysis of exploration. The outcomes of the experiments reveal that the model is robust and has a high level of generalization with a classification accuracy of 96.77, AUC of 0.9853, training loss of 0.05 and testing loss of 0.065. The results make the system a scalable and viable automated content moderation solution, which can help to build a safer online environment.