The modern world is advancing along a digital revolution. Internet is everywhere and everyone is dependent on it. It’s obvious that industries should also be digitalized. Such a transition is represented by Industry 4.0. A lot of new technologies play a vital role in Industry 4.0. The main advantages of industry 4.0 include flexibility, sustainability, efficiency, increased productivity, and so on. The biggest problem in implementing Industry 4.0 is the skillset of employees. As the employees are trained to work in a physical environment, a sudden transformation to digital requires significant skill upgrading. Skills can be upgraded by providing proper training, support, and encouragement. As many technologies stand as pillars for industry 4.0, this chapter focuses the role of Artificial intelligence (AI) and deep learning. Due to the increased amount of data, deep learning concepts are often too demanding to be implemented on a large-scale industry as they also consume more time. To address this issue, distributed deep learning is necessary, which enhances the stability and utilizes the resources efficiently. With an increase in distributed deep learning methods, it is inevitable to design an AI model to clearly elucidate these methods to users, that is, an eXplainable AI (XAI) for the deep learning algorithms. By integrating these two technologies and implementing them in Industry 4.0, creating an innovative approach to increase productivity and digitize industries becomes a hassle-free process. This chapter proceeds with summarizing the deep learning concepts accompanied with its eXplainable AI (XAI) model in a distributed environment, along with its highlights and challenges.