The total cement energy consumption is around 5% of global industrial energy usage. In cement plants, mills consume half of this energy for dry grinding particles. However, grinding in tumbling mills is a random process, and a maximum of 5% of this energy would be directly devoted to particle size reduction. Thus, understanding interactions between operation variables and the mill energy consumption factors would be essential for sustainable cement production and green transition. Surprisingly, few investigations were conducted to study the energy consumption indexes of cement mills. Using a conscious lab “CL” as an advanced AI structure for industrial-scale problems could facilitate such an understanding of interactions within cement mill variables and promote controlling energy consumption for sustainable production. To fill the gap, this study developed a CL by examining different AI models (Random Forest, Support Vector Regression, Convolutional Neural Network, extreme gradient boosting, CatBoost, and SHapley Additive exPlanations) for modeling energy consumption indexes of a close ball mill circuit in a cement plant to address the effectiveness of operating variables. Explainable AI modeling highlighted interactions and measured the effectiveness of operating variables on mill energy consumption indexes. The airlift current and separator variables ranked the most effective operating factors on the mill energy consumption indexes. CatBoost, as an advanced AI model, showed the highest prediction accuracy for modeling (R2: 0.90). Such a CL model for a cement mill can be used for training operators, controlling the process, saving time and energy, reducing laboratory work, and scaling issues, and finally enhancing sustainability.