To maintain high standards and efficiency in the age of Industry 4.0, automated production lines must have real-time quality monitoring. Using deep learning techniques to improve real-time quality control systems is the focus of this essay. Various industrial settings employ state-of-the-art deep learning algorithms to discover flaws and problems. We provide a complete evaluation of these methods, including CNNs, RNNs, and GANs. Using a combination of experimental and case study methodologies, we show how these algorithms may be used in conjunction with current automation systems to make quality evaluation more efficient and accurate. Deep learning enhances production line efficiency via improved decision-making and enhanced issue detection accuracy, according to the results. In addition, we go over some of the obstacles and potential solutions for a widespread use of these technologies in the future.