This review delves into the dynamic realm of sign language detection, offering a meticulous exploration of techniques, challenges, and promising directions. Essential for empowering communication among individuals with hearing impairments, sign language detection intersects with cutting-edge technologies. The paper meticulously surveys current methodologies, spanning computer vision, deep learning, and sensor-based innovations. By dissecting challenges like gesture variability, real-time processing constraints, and dataset diversity, the review provides profound insights. Solutions and future trajectories, including advanced neural network architectures and multimodal sensor fusion, are scrutinized. The synthesis of existing knowledge aims to inspire further research, bridging gaps, and propelling the evolution of sign language detection technology.