Censoring schemes play a pivotal role in survival analysis, particularly within the framework of competing risks models. These models are essential in analyzing time-to-event data where multiple potential failure causes exist, and not all events are fully observed due to censoring. Various censoring schemes, including Type-I, Type-II, hybrid, and progressive censoring, have been developed to handle incomplete data, providing robust methodologies for estimating survival functions and hazard rates. This review explores the theoretical underpinnings and practical applications of these censoring schemes in competing risks models. Emphasis is placed on recent advancements in the field, highlighting their significance in a range of disciplines, from reliability engineering to clinical trials. The study aims to provide a comprehensive overview of how different censoring schemes impact the analysis of competing risks, offering insights into their strengths and limitations. By synthesizing the latest research, this paper contributes to the ongoing discourse on improving the accuracy and applicability of survival analysis in the presence of competing risks.