Object detection is a primary research area in the computer vision field, which employs models such as YOLOv3 and SSD to achieve radical technological breakthroughs. These models are excellent at the locations of an object and the fact that it is handled different layers of patterns which make it attractive for real-time use, which can detect common objects with 88% accuracy and has a mean average precision (mAP) of 57.9% on the COCO dataset. Presently, only the addition of the text-to-speech (TTS) system is enabling object detection to be brought up in more interactive and user-focused areas. This paper is mainly about the YOLOv3, and SSD models and it discusses their ability, speed, and an online environment. Additionally, it demonstrates how newer CNN-based designs facilitate us to understand the information in the pictures/camera sensors, as well as the comparison of them with other such methods that are the combination of text-to-speech synthesis with object detection for tasks in e-commerce, education, and automation. This research provides some basics of the object detection systems as well as combined them with text-to-speech (TTS) technology which enhances user interaction and accessibility. The study deals with the questions on the topics of correct identification and misuse of object recognition technologies in a environment. And during the research, the process of developing and operating a TTS-system integrated online platform is used. The application of the TTS-system together with the object detection reached a very high stage as the interactive data system supplied correct and up-to-date information and spoke to end-users without necessitating hand activities in various places like e-commerce, education, and automation. This paper looks at real-world uses and challenges, encouraging the use of these technologies while pointing out areas for research to improve detection models and speech systems for accessibility solutions.