Nowadays with the emergence of Industry 4. 0, there is therefore the need to have sustainable manufacturing in order to increase operational efficiency as well as relieve the pressure on the environment. A potential weakness of conventional approaches is time-dependency while processing real-time data to prevent dysfunctions in the company’s resource management, energy consumption control, and predictive maintenance. Thus, in this work it is proposed an innovative approach to address these problems with the use of Deep Reinforcement Learning (DRL) and Internet of Things (IoT) technology. Energy consumption, machine performance and environmental data are collected by IoT sensors in real-time and fed into a DRL model. This concept effectively eliminates the vices that are characteristic of conventional approaches in that it seeks to optimize production processes by cell design. The approach suggested above attains an unprecedented 98. 2% accuracy rate. This is in a bid to exemplify its capability in predicting these phenomenal and balance forecasts, to improve the overall performance and sustainability of manufacturing. Thereby, the method offers a robust answer to the acute questions arising in front-line manufacturing today and sets a brand-new reference for intelligent and sustainable behavior in the context of the fourth industrial revolution 4.0.