This research seeks to develop a translation system that fills the native tongue of Iraqi slang language to Iraqi Sign Language (ISL) using smartphone and IoT technology. Language barrier between Iraqi slang and ISL is an issue in communication especially for the hearing-impaired portion of population because Iraqi slang is different from formal Arabic and possess unique words and tonality. The inability to translate slang does not allow deaf individuals to partake in conversations that use it. We then propose an innovative IoT based translation system that uses smartphone capabilities for seamless real time communication to handle this issue. The system is based on voice recognition, feature extraction and classification techniques to transform spoken Iraqis slang to ISL hand gestures. The speech tone, pronunciation and background noise variations are compensated with a combination of noise reduction algorithms and a threshold-based classifier so that it works accurately. To evaluate the system in a real-world scenario, it was tested under several noise levels. The classification result shows a high accuracy of the classification of 94%, and that it can be used as a practical tool to increase accessibility for the hearing-impaired community. With the introduction of this proposed solution, this gives a new approach to how linguistic flaws are addressed through the use of current technology in order to create more inclusive and simple communication between the hearing and non-hearing individuals. The vocabulary set is extended for future research, and deep learning is integrated into the classification process for increased performance. The real time processing capabilities are improved further.