Hate speech creates a severe obstacle to social unity and personal wellness during the modern connected digital era. An extensive understanding of natural language processing methods is needed because this knowledge helps in determining the detection of various hate speech types present on social media platforms. A discussion of capabilities of advanced models that exists to detect hate speech in sentences gathered from multiple communication platforms forms the core of this research. A meticulously curated dataset consisting of 451,709 English sentences incorporating emojis, slang, and contractions is utilized in this paper. A particular model demonstrates remarkable effectiveness among all its competitors based on the evaluation results. In addition to the technical emphasis on detection, the empirical research examines the ethical consequences that comes with using such detection systems. Research findings demonstrate effective methods to improve harmful content moderation practices since they may substantially enhance online security-measures.