This study focuses on improving Recurrent Neural Networks (RNNs) utilized for ASL alphabet recognition system. RNNs are particularly effective in capturing sequential patterns and temporal dependencies in gesture data, allowing them to effectively process video frame sequences and classify hand gestures with high accuracy. However, RNN-based systems face a fundamental problem wherein they struggle to distinguish letters with nearly identical hand shapes, such as 'i' and 'j', 'u' and 'v', or 'a', 'e', 'm', 'n', 's', and 't', which can appear nearly identical in 2D representations. Implementing enhanced feature extraction mechanisms with 3D spatial encoding and specialized fist/thumb discrimination features captures subtle finger articulation differences and palm positioning, improving the system ability to distinguish between visually similar ASL alphabet signs through higher-resolution features and attention-based mechanisms. Using a dataset of 1,300 video samples (50 per letter), results demonstrated a dramatic improvement in distinguishing similar letters, with the enhanced model achieving 98% accuracy for fist-based letters (A, E, M, N, S, T) compared to the existing model's 45% accuracy, representing a 97% improvement. These findings demonstrate that enhanced RNN, applied to ASL alphabet recognition, effectively boosts the performance and robustness of hand gesture recognition and classification tasks, offering a reliable solution for ensuring accuracy and reducing misclassification in sign language interpretation systems.