The work presented in this paper aims to develop an innovative system that translates tongue gestures into meaningful English sentences. The proposed technology will enable students with severe disabilities to interact with and control their environments simply by moving their tongues. The system will incorporate the use of digital signal processing and machine learning techniques to identify and analyze tongue movements. The Tongue Motion Translator (TMT) will assist people with severe disabilities to communicate effectively with each other and their environments. The work addresses the challenge of converting complex tongue gestures into expressive linguistic output through a robust and effective framework. It introduces improvements to the existing design to hopefully enhance the robustness and accuracy of the system. The new model introduces the use of the Arduino Nano 33 Ble sense as the controller and a new sensor array setup consisting of the four TLV493D 3 axial magnetometers for sensing the magnetic field generated by the magnet. The proposed system builds on the existing five tongue gestures (up/down or left/right tongue movements) by introducing new gestures and also frequency tracking, i.e., multiple movements translate to one single gesture.