Object detection is a critical task in computer vision with applications ranging from surveillance to autonomous driving. Deep learning methods have shown remarkable success in this area due to their ability to learn complex patterns from data. In this paper, we propose a system that utilizes deep learning techniques for real-time object detection, coupled with voice output for enhanced accessibility. We present a comprehensive literature review, outlining the evolution of object detection methods, focusing on deep learning approaches. Our system employs state-of-the-art deep learning models and techniques for accurate object detection, and integrates text-to-speech functionality to provide auditory feedback of detected objects. We evaluate the system's performance on standard datasets and demonstrate its effectiveness in real-world scenarios.