The development of smart manufacturing systems is being driven by a variety of diverse needs for the dependability of equipment and the prediction of quality. In order to accomplish this objective through the use of machine learning, a wide range of approaches are being investigated. The management and protection of one's company's data presents yet another challenging aspect of doing business. In order to cope with fraudulent datasets, machine learning and internet of things technologies were utilised. These technologies were used to protect system transactions and manage a dataset. Because of this, we were able to find solutions to the problems that we had previously discussed. The gathered information was organised and examined with the help of big data techniques. The Internet of Things system was constructed using the Hyperledger Fabric platform, which is a private computer network. In addition, a hybrid prediction strategy was utilised for the defect diagnostic as well as the defect forecasting. The latest machine learning techniques were utilised in order to model the complexity of the environment and estimate the genuine positive ratio of the quality control system. The quality control of the system was evaluated using these pieces of data.