Deep learning, a new paradigm in computer vision, has drawn significant interest from the research associations and shown a higher potential than traditional approaches. Convolutional neural networks (CNN), one of the profound learning concepts, have gained today’s research by achieving superior outcomes in image recognition and classification. As one of the smart investigations, CNN has been applied in many applications, including health care and safe transportation. Nowadays, much of the research on deep CNN focuses on efficiency while detecting and classifying the objects from a given input image with a greater accuracy level. This process can contribute to the learning approaches in the fields of computer vision, machine learning, artificial intelligence, and deep neural networks. This paper proceeds with object detection, prediction, and classification based on convolutional networks to examine the present state-of-the-art technologies and applications like disease detection and smart vehicles. The survey outcomes are presented based on the architectures, neural networks, and deep reinforcement paradigms.