Digital platforms have spread fast and widely worldwide, making it unprecedented to connect socially and communicate. The fast development of digital technology, however, has enabled the spreading of hate speech communication that creates violence, prejudice, or hostility against someone because of attributes including gender, religion, race, religion, gender, or ethnicity. Hate speech also has significant social implications, including societal fragmentation, reinforcing harmful stereotypes, and causing psychological harm to individuals and communities. Thus, it has become a critical challenge for social media platforms to identify and mitigate hate speech. This research highlights the need to develop automated hate speech detection techniques to make online content moderation at scale feasible. This research paper critically evaluates the existing approaches for hate speech detection, specifically those revolving around Machine Learning and Natural Language Processing and proposes a novel ensemble voting classifier approach that can improve the accuracy, fairness, and transparency of hate speech detection systems. Some mere contributions of this paper are as follows: In conclusion, this research focuses on the abilities of hate speech detection systems along with underscoring their unbending importance in fostering online safety, fairness and reliable communication in the digital world. The instincts and findings gained through this research pave the roadway for future innovations and practical applications aimed at being more respectful and responsible towards other online spaces.