Integrating developing technology and data analytics in smart grids has raised apprehensions over security and privacy. This review article examines the techniques and procedures employed in secure data analytics for smart grids. It uses these approaches to improve grid monitoring, discover anomalies, and make accurate forecasts. It also emphasizes the need to protect data privacy and maintain confidentiality throughout the process. The study encompasses several focal areas: homomorphic encryption, secure multi-party computation, and specific data analytics about smart grids. This study examines the safeguarding of encrypted grid data using machine learning techniques. It explores the many obstacles encountered in the design and execution of such measures within the context of the smart grid domain. Through the synthesis and critical examination of pertinent scholarly literature, this research elucidates the capacity of secure data analytics to detect anomalies, forecast grid patterns, and enhance decision-making procedures. Additionally, the analysis underscores the presence of undiscovered domains and potential avenues for future research in safe data analytics for smart grids. This extensive review is a significant reference for researchers, professionals, and politicians seeking to understand and enhance the methods that guarantee the privacy and security of smart grid operations.