In today’s society, individuals with visual impairments face major obstacles when trying to access visual information. This paper explores recent advancements in real-time image captioning combined with voice output technologies, developed to help bridge this accessibility gap. Utilizing deep learning techniques—specifically Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units—these systems are designed to interpret images and generate spoken descriptions, making visual content more accessible. The review highlights current methodologies, key challenges, and presents an integrated framework for a real-time image-to-audio system tailored to the needs of visually impaired users. By incorporating attention mechanisms, speech synthesis, and other innovative features, the goal is to enhance user interaction and broaden the scope of assistive tools so that visually impaired individuals can move freely without others support.