Speech and hearing disabilities pose significant challenges, especially in the absence of sign language interpreters or assistive technologies. This paper focuses on the design and implementation of a Smart Glove—the first wearable low-cost real-time gesture recognition system that translates a person’s hand and finger movements into synthesized speech and text. The Smart Glove is equipped with a three-axis accelerometer, five flex sensors, and a 16-channel analog multiplexer. Each of these components is connected to a NodeMCU microcontroller which is responsible for data collection and sending the data wirelessly. Unlike competing products that use high-cost external sensors or External Processing Units, our glove’s circuitry features a dense and low power design that is self-sufficient and optimized for long-term, effortless use to ensure precise recognition of the gestures performed with the hand in different orientations and multiple hand positions, a machine learning-based gesture recognition algorithm trained on dynamic hand gesture data is applied to the microcontroller. Testing of the glove involved sign language alphabets and words, achieving over 90% accurate recognition in real-time use. The Smart Glove also offers the ability to provide audio feedback in multiple languages and can be tailored to different user needs.