For text and speech translation, the system will enable gesture-to-text and gesture-to-speech translation, enhancing accessibility across devices and environments. This translation concept can explore the utility of deploying a feasible deep learning active model for non-dynamic hand motion and gesture cognition as a first step towards real-time mobile applications. The paper focuses on measuring the MobileNetV2 employing a 37-class static American symbols for hand gesture dataset by comprising digits (0–9) and alphabets (A–Z) with the special character such as underscore (_), targeting to notify a suitable model for real-time reasoning under commutative constraints. The proposed model trained on pre-processed data and augmented input image data (224 × 224 resolution). Primary outcomes showed the proposed model acquired range over 99% of the training as an accuracy with 5–6 running epochs, mentioned signs of overfitting. Regularization techniques, for example dropout, weight decay, and data augmentation were applied, resulting in stabilized training and validation accuracy between 98% and 99% by epoch 10. These results outperform several baseline models and confirm MobileNetV2’s suitability for lightweight gesture recognition tasks. Given its high accuracy and low complexity, MobileNetV2 is selected as a back-end candidate for future mobile integration. The next phase will extend the model to dynamic hand gesture recognition using sequential data and temporal deep learning architectures.