The precise development of automated recognition systems for Indian Sign Language (ISL) faces significant difficulties because ISL gestures demonstrate high variability together with complex patterns. Classic neural networks fail to grasp both the space-focused together with time-based properties of these gestures appropriately. Our proposed model uses Convolutional Neural Networks (CNN) to extract spatial features and a Long Short-Term Memory (LSTM) network with a percentage-based attention system to analyze temporal elements. The system analyzes frames through the CNN networks and employs Attention-LSTM temporal processing to achieve 99% accurate ISL gesture recognition on a complete dataset. This research presents a CNN-Percentage-Based-Attention-LSTM model structure which effectively retrieves gesture space and motor characteristics while achieving better accuracy levels as compared to traditional approaches. The attention mechanism embedded in the model helps it direct attention toward essential gesture features to enhance recognition accuracy on both complex and subtle gestural movements. Real-time ISL gesture recognition’s scalability and robust nature enable the model to operate as a promising communication aid tool for educational, social, and professional domains and hearing-impaired individuals. The obtained results show how this methodology can solve the present challenges of standard ISL recognition techniques while leading to new developments in this field.