Speech-to-text (STT) technology converts spoken language into written text, utilizing a multi-step process that involves audio input, signal processing, phoneme recognition, language modeling, and text output. Deep learning models have significantly improved STT accuracy and efficiency, making it accessible through advanced online platforms. On the other hand, Text-to-speech (TTS) technology converts written text into spoken language, employing a multi-stage process that includes text analysis, linguistic processing, speech synthesis, and speech rendering. TTS systems rely on natural language processing capabilities to generate human-like voices, with some models capable of adjusting pitch, speed, and volume for customization. Both STT and TTS have diverse use cases across various domains, including transcription, dictation, voice assistants, accessibility, education, media, entertainment, customer service, and language learning. While both technologies offer advantages, such as time-saving and efficiency, they also have limitations, including accuracy concerns and limited language support in some systems. The future of STT and TTS is promising, with ongoing research and development pushing the boundaries of what's possible, particularly with the emergence of speech-language models that can perform both STT and TTS simultaneously.