<p>Hate speech detection is a growing area of research driven by the increasing spread of harmful content on social media platforms. Automated systems are needed to identify and manage such content, particularly in low-resource Indian languages such as Hindi, Tamil, Malayalam, Telugu, and others, which remain underrepresented in Natural Language Processing (NLP) research. We conducted a Systematic Literature Review (SLR) focused on hate speech detection in these languages to assess recent developments and challenges. We retrieved 2,372 records from databases including ScienceDirect, IEEE Xplore, ACM Digital Library, Google Scholar, and arXiv, and selected 61 studies based on predefined inclusion criteria. The review highlights significant challenges such as limited annotated datasets, class imbalance, and the difficulty of modeling socio-linguistic diversity. Although multilingual and code-mixed models show promise, they often fail to capture regional language variations and emerging forms of online hate speech. Ethical concerns about bias, fairness, and transparency in automated systems are also addressed. Additionally, the influence of social norms, language variation, and platform-specific policies on model performance is discussed. This review offers valuable directions for future research, emphasizing the need for culturally informed models, better data resources, and methods to address linguistic diversity. The goal is to support the development of fair and inclusive hate speech detection systems that protect vulnerable communities while upholding freedom of expression.</p>