This work offers a thorough analysis of developments in the field of ISL (Indian Sign Language) recognition. In order to address the communication difficulties that the deaf population faces, this article examines the many approaches and technologies used in sign language recognition products. It emphasizes the application of conventional statistical methods like Principal Component Analysis (PCA) and Hidden Markov Models (HMM), and modern machine learning techniques, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The study highlights the use of ISL for real-time recognition while pointing out that deep learning techniques are useful for increasing accuracy. Several studies are reviewed, showcasing successful implementations of ISL recognition systems with varying degrees of accuracy and robustness. Key challenges identified include dataset limitations, gesture variability, occlusions, and real-time processing requirements. The paper concludes by advocating for future research to enhance dataset diversity, improve model interpretability, and develop real-time applications, aiming to bridge communication gaps and promote inclusivity for the deaf and hard-of-hearing community. Future research aims to enhance system accuracy, extend real-time capabilities, and improve the reproducibility and reliability of findings in ISL recognition.