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Communication barriers faced by individuals with hearing and speech impairments remain a critical challenge in promoting inclusive human interaction. This paper presents a novel real-time sign language detection and emergency response system that integrates computer vision-based gesture recognition with simultaneous demographic and affective analysis. The proposed framework leverages OpenCV along with deep learning architectures to enable robust, multi-modal recognition of hand gestures corresponding to standard sign language alphabets and words, while concurrently performing age estimation, gender classification, and emotion. The system employs a convolutional neural network (CNN) trained on augmented datasets to accurately classify static and dynamic hand gestures under varying illumination and background conditions. Facial analysis modules, built upon pre-trained models fine-tuned for real-world scenarios, provide reliable demographic profiling and emotional state recognition. A key contribution of this work is the integration of an emergency response module that detects predefined distress signs and critical emotional states, automatically triggering alert notifications to designated contacts or emergency services, thereby extending the utility of the system beyond communication facilitation to personal safety.Experimental evaluations demonstrate that the proposed system achieves a gesture recognition accuracy of approximately 94.7%, with age estimation and gender classification accuracies of 89.3% and 96.1%, respectively, and an emotion recognition rate of 91.5% across six primary emotional categories. The system operates at real-time processing speeds of 28–32 frames per second on standard computational hardware, confirming its suitability for practical deployment. The unified pipeline reduces the need for multiple standalone systems and offers a scalable, accessible solution for assistive technology, healthcare monitoring.