<p>Hate speech, often expressed through language that demeans, marginalizes, or promotes violence against individuals or groups based on traits like race, gender, religion, or ethnicity, poses a serious threat to online discourse. With the rapid expansion of user-generated content across digital platforms, hate speech has evolved beyond simple text, presenting significant challenges for automated detection. These difficulties are further amplified in multilingual and multimodal settings, where understanding context, cultural subtleties, and regional dialects is essential for accurate identification. Existing approaches fail under inherent biases from biased data culturally-incentive annotation and algorithmic constraints. Moreover, most systems are accuracy optimized at the cost of fairness, explainability and inclusiveness. To address these limitations, this review provides a systematic and thorough review of recent breakthroughs in multilingual hate speech detection (HSD), multimodal fusion techniques and bias reduction techniques. The contribution of this paper is in proposing unified tri-intersectional paradigm that unites all three aspects, multilingualism, multimodality and fairness. This paper delves into the intersection of these aspects and influence each other, determine gaps in existing benchmarks and models and emphasize the necessity of holistic, culturally-sensitive assessment protocols. Through comparative synthesis, we compare existing approaches based on scalability, fairness and real-time application. This review concludes with open research challenges and directions such as the need for inclusive datasets, fairness-aware training, explainability tools and ethical guidelines for automated moderation. The proposed work aims to guide researchers and practitioners towards developing robust, equitable and context aware HSD systems.</p>