The prevalence of hate speech on digital platforms poses a significant challenge to the upkeep of a welcoming and secure online community. In order to improve hate speech detection (HSD) and encourage explainability and openness in the decision-making process, this study introduces a decision support system (DSS). The DSS uses state-of-the-art machine learning algorithms and natural language processing methods to assess and classify text-based material. This study covers the system creation process, which includes feature engineering, model selection, data gathering, and preprocessing. To efficiently classify hate speech, the DSS mainly depends on sentiment analysis, context awareness, and deep learning models. This ensures that human reviewers can understand and validate the model's predictions through the use of explainability approaches like attention mechanisms, rule-based systems, and feature importance ratings. In conclusion, this study outlines the development of an explainable hate speech detection decision support system and emphasizes the need for transparency and accountability in addressing this urgent social issue. The system can be an effective and moral weapon that online platforms, content moderators, and legislators can employ to combat hate speech and promote a more responsible and inclusive digital society.