In today’s competitive scenario, the manufacturing industries are lagging behind in implementing the Industry 4.0 concept or in integrating smart, ubiquitous components due to high cost and high energy consumption due to the volatile market. The digital transformation has reshaped the manufacturing industries and has paved the way towards data-driven, intelligent, networked, and resilient manufacturing systems. In this context, Industry 4.0 is progressing exponentially and offers a productive output in terms of circular economy and cleaner production to attain ethical business by achieving accuracy, precision, and efficiency. Hence, there is a strong requirement to revamp the traditional manufacturing set-ups into smart manufacturing to gain self-adaptability, reliability, and flexibility with high quality and low-cost output. The paper proposes a mixed-integer linear programming (MILP) model for Industry 4.0 set-up to achieve circular economy and cleaner production by optimizing products-machine allocation. The proposed MILP model optimizes the trade-off between energy consumption and machine processing cost to gain a circular economy and cleaner production respectively. The proposed model also achieves ethical business by deploying sensors to capture real-time information to establish the Industry 4.0 facility. The paper discusses the product-machine specific analysis to optimize the manufacturing of customized and high-end products at low production cost with minimal energy consumption. The objective of the paper is to minimize the total cost and energy consumption of machines for establishing the Industry 4.0 facility to gain a circular economy and cleaner production. The proposed model is demonstrated and computationally tested on small, moderate, and large data instances and presented with their detailed analysis.