Vision is a precious sense, but unfortunately, loss of sight is becoming increasingly common (World Health Organization 2002) [1] in today’s world. Blind persons faces substantial encounters, as they lack awareness of the potential dangers around them in daily life. To assist them, there is a need to translate the visual environment into an audio format that can effectively inform them about nearby objects. Visually impaired individuals encounter numerous difficulties, even within familiar surroundings, due to a lack of detailed information about their environment. This project addresses this issue by utilizing a CNN for recognizing objects from pre-trained datasets. Specifically, it leverages deep learning, using a DNN to identify targets captured from real-world environments. The images captured are compared against pre-trained objects stored in a dataset, with object recognition based on shape and size. It offers a practical solution for navigation and object recognition, achieving a classification accuracy of 85–90% across common objects like furniture, pedestrians, and vehicles. The deep neural network, built using TensorFlow, incorporates a model known as Mobile Net SSD, which compares real-time captured images with pre-trained objects based on their features. Once a match is identified, the name of the object is displayed and subsequently converted into an audio output using Google Text-to-Speech (gTTS). This enables visually impaired individuals to understand the objects present in front of them through auditory feedback.