As hate speech has increased across digital platforms, worries over its impact on social discourse and potential to incite violence and bigotry have grown. Therefore, service providers and researchers must recognize and control hate speech. In this study, we examine research that examines several facets of identifying hate speech that was published during 2017 and 2023. The start of this assessment highlights the concerning rise in hate speech on the World Wide Web and its negative impacts, emphasizing how crucial it is to create trustworthy identification systems. We categorize the studies into five overarching topics based on their primary areas of interest: dataset creation, bias analysis, algorithm development, multilingual and multimodal approaches, and ethical issues. In addition to highlighting the shortcomings of the datasets and identification techniques for hate speech that are currently available, to address the dynamic nature of hate speech, this comprehensive literature review along with meta-analysis also highlights the necessity for larger datasets, reliable algorithms, and standardized devaluation measures. This thorough assessment offers a detailed analysis of the hate speech detection landscape, which will be of interest to researchers, legislators, and IT businesses, as a useful resource for efficiently combating online hate speech. Subsequent investigations into this important subject may expand on the challenges and insights presented here.