Indian Sign Language (ISL) Recognition is one of the research concerns that seeks to solve the communication inconvenience that the hearing-impaired persons go through in their everyday lives. In this regard, we present a comprehensive review of ISL Recognition literature between 2011 and 2024 highlighting various research developments, methodologies, and technologies that have improved the accuracy and efficiency of recognition systems. The review also discusses three main issues: data acquisition, data environment, and sign language representation. Our analysis of the selected studies shows that each study has its strengths and weaknesses although they all achieve notable results. Finally, the paper traces methodologies from traditional sign language gesture identification techniques to advanced deep learning approaches. Based on the performance evaluation of ISL systems, CNN models have achieved recognition accuracy varying from 79 to 99.93%, while HMM models report accuracy between 80.4 and 98.23%. Other methods such as SVM, LSTM, KNN, ANN, and SIFT demonstrate accuracies ranging from 75 to 99%. Moreover, the paper highlights major challenges researchers face including variation in sign language dialects, real-time application, and environmental diversification impact on ISLR models. This survey aims to be a valuable resource for researchers and practitioners, inspiring further innovations in Indian Sign Language recognition.