This research aims to develop an intelligent framework for quality control and fault detection in pre-production and post-production systems in Industry 4.0. In the pre-production system, the health of the manufacturing machine is monitored. In this study, we examine the gear system of induction motors used in industries. In post-production, the product is tested for quality using a machine vision system. Gears are fundamental components in countless mechanical systems, ranging from automotive transmissions to industrial machinery, where their reliable operation is vital for overall system efficiency. A faulty gear system in the induction motor directly affects the quality of the manufactured product. Vibration data, collected from the gear system of the induction motor using vibration sensors, are used to predict the motor's health condition. The gear system is monitored for six different fault conditions. In the second part, the quality of the final product is inspected with the machine vision system. Faults on the surface of manufactured products are detected, and the product is classified as a good or bad product. The quality control system is developed with different deep learning models. Finally, the quality control framework is validated and tested with the evaluation metrics.