Hand sign recognition systems play a crucial role in bridging communication gaps for people with hearing and speech impairments. This review paper explores various methodologies and algorithms employed in previous research on hand sign recognition, analyzing their performance, accuracy, computational efficiency, and effectiveness in real-world applications. Special emphasis is given to algorithms related to the Discrete Fourier Transform (DFT), including the Hebbian Classifier, Radial Basis Function (RBF) networks, and Self-Organizing Maps (SOMs), which have been utilized for feature extraction, pattern recognition, and classification. The study also examines deep learning approaches such as Convolutional Neural Networks comparing their strengths and limitations. Additionally, the paper highlights how these advances contribute to assistive technologies in healthcare, aiding doctors during medical procedures, and improving accessibility for individuals in need. By providing a comparative analysis of these techniques, this review aims to offer insights into the most effective strategies for enhancing hand sign recognition systems, paving the way for future research and innovation in the field.