aspg-20-2054-1791307730 20261006172850 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Wireless and Ad Hoc Communication IJWAC 2692-4056 2023 7 1 Ahmed Hatip Gaziantep university, Turkey Karla Zayood Online Islamic University, Department Of Science and Information Technology, Doha, Qatar Rabah Scharif Applied Engineering Department, Institute of Applied Technology, UAE The convergence of Industry 4.0 and sustainability has brought forth a new era of manufacturing, where data-driven approaches play a pivotal role in achieving operational efficiency while minimizing environmental impact. This paper presents an innovative framework for sustainable smart manufacturing through data-driven predictive maintenance planning. By integrating advanced analytics and machine learning, we propose a preemptive equipment management approach that not only optimizes production processes but also fosters environmental responsibility. Our methodology combines the power of Long Short-Term Memory (LSTM) networks for pattern modeling and the Sea Lion Optimization Algorithm for feature selection. We demonstrate the effectiveness of our approach through a comprehensive empirical analysis conducted on a real case study, where the results indicate significant improvements over baseline studies, as evidenced by reduced Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), along with higher R-squared (R2) values. Our findings emphasize the synergy between technological innovation and sustainability imperatives, positioning our approach as a catalyst for reshaping modern manufacturing practices. 2023 40 49 2054 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJWAC.070104 https://americaspg.com/journal/20/article/2054 https://americaspg.com/storage/11694236391.pdf