The industrial sector remains one of the largest energy consumers, with a heavy reliance on fossil fuels that contribute significantly to global greenhouse gas emissions. Additionally, most studies focus on one machine component rather than a whole system that considers multiple influencing factors, such as spindle speed, feed rate, and washdown pump operations. This gap highlights the need for an advanced, data-driven system capable of continuous energy monitoring, analysis, and optimization. In this paper, authors propose a dual-Raspberry Pi architecture for an industrial internet of energy-based monitoring system for real-time energy analysis and optimization in five-axis machine tools to address these challenges. The system employs Modbus RS485 communication to facilitate efficient data acquisition and visualization. Energy consumption patterns are further analyzed using the Affinity Law to optimize washdown pump operations. Experimental findings demonstrate that decreasing the washdown speed from 3500 to 2900 rpm results in a 43.2% reduction in energy consumption, saving approximately 2.216 kWh per day and reducing CO₂ emissions by 1.097 kg. Additionally, reducing the feed rate from 100 to 50% significantly enhances spindle energy efficiency, achieving the greatest savings of up to 62.5% without compromising machining performance. The proposed real-time monitoring and optimization system effectively reduces energy costs, minimizes carbon emissions, and promotes sustainable manufacturing practices.