Distributed neural network training for intelligent manufacturing systems is perhaps the most significant player in the field of Industry 4.0 regarding its function and application. The biggest power users in the industry of the era, such as IoT, big data analytics, and AI, set up intelligent, automated, and capable production settings. On their part, roles in doing data analysis, predictive maintenance, quality control, and process optimization seem to play a pretty significant role in these systems. Actually, it starts with presenting a short outline of the smart manufacturing systems and the importance of neural networks. The paper heavily focuses on the fact that parallel training is the best approach toward these enormous amounts of data; however, it further points out that the real advantages of this technology are upgraded scalability and decreased training time. To begin with, some overview concepts of distributed deep learning are made, referring to differences of basic and distributed training, and also to the role of components within the distributed systems-that are computing nodes, communication protocols, and synchronization mechanisms. Taking it forward, this paper takes up the distributed neural network training technology that uses knowledge sharing such as data parallelism, model parallelism, and hybrid parallelism along with case studies on different projects. The building blocks of distributed training like cloud computing, fog computing, edge computing, and networking are looked at. In this section, implementation strategies are discussed in association with a list of popular frameworks and tools describing the process involved in setting up a distributed training environment. Challenges of distributed training including data management, synchronization, communication overhead, scalability, and fault tolerance are explained. The final section of the course shows how performance might be optimized, including efficient data sharding, gradient compression, asynchronous training, and utilization of specialized hardware.