This article presents the design of a communication tool that employs machine learning to facilitate seamless interaction between deaf individuals and those who do not understand sign language. The tool translates sign language gestures into written text and converts spoken language into text, effectively bridging the communication gap. By utilizing machine learning the system accurately interprets hand movements to translate sign language gestures. Automatic speech recognition (ASR) methods based on recurrent neural networks and long short-term memory models convert spoken language into text. The tool features a very simple user interface, visual feedback mechanisms, and adaptive learning algorithms, taking into account the specific needs of deaf users. Experimental evaluations demonstrate its effectiveness, making it a valuable aid for enhancing accessibility and inclusivity for the deaf community across various domains.