This paper presents an extensive review of various machine learning and deep learning methodologies applied to the detection of cyberbullying in Hindi text. With the proliferation of social media and digital communication, cyberbullying has emerged as a significant concern, especially in multilingual contexts like India. This study delves into a range of algorithms, from traditional machine learning models to advanced deep learning architectures, evaluating their efficacy in identifying bullying patterns and behaviors in Hindi language datasets. We provide a comparative analysis of methods such as Support Vector Machines, Naive Bayes, Random Forest, and more contemporary approaches like Bidirectional GRUs and Transformer-based models like BERT, MuRIL. The results highlight the challenges and breakthroughs in adapting these technologies to Hindi, a language with rich linguistic diversity and complexity. Our findings aim to guide intend to direct and enrich future research efforts in crafting more sophisticated and context-sensitive models. The ultimate goal is to foster the development of more effective tools for combating cyberbullying, thereby enhancing the safety of online communities.