The paper presents an innovative hybrid algorithm that integrates the Arithmetic Optimization Algorithm (AOA), Simulated Annealing (SA), and new search strategies to optimize the hyperparameters of LightGBM for industrial fault warning. The proposed algorithm, named SAOA, aims to enhance the computational efficiency and accuracy of LightGBM in various industrial settings. The effectiveness of the SAOA algorithm is demonstrated through real-world case studies, showing superior performance compared to advanced methods such as ISEO-BP, LSTM-CNN, and GWO-LightGBM. The SAOA algorithm achieved a fault warning accuracy rate of 90% in a water pump application, outperforming other methods by reducing Root Mean Square Error (RMSE) by 7.14%, 17.84%, and 13.16%, respectively, and increasing R-Squared values by 2.15%, 7.02%, and 3.73%. The paper also discusses the contributions of the proposed method, including the introduction of a pioneering hybrid algorithm, an improved LightGBM model, and rigorous real-world validation. The results highlight the potential of the SAOA-LightGBM algorithm in enhancing fault warning systems, with applications in various sectors such as transportation, energy, and finance.The paper presents an innovative hybrid algorithm that integrates the Arithmetic Optimization Algorithm (AOA), Simulated Annealing (SA), and new search strategies to optimize the hyperparameters of LightGBM for industrial fault warning. The proposed algorithm, named SAOA, aims to enhance the computational efficiency and accuracy of LightGBM in various industrial settings. The effectiveness of the SAOA algorithm is demonstrated through real-world case studies, showing superior performance compared to advanced methods such as ISEO-BP, LSTM-CNN, and GWO-LightGBM. The SAOA algorithm achieved a fault warning accuracy rate of 90% in a water pump application, outperforming other methods by reducing Root Mean Square Error (RMSE) by 7.14%, 17.84%, and 13.16%, respectively, and increasing R-Squared values by 2.15%, 7.02%, and 3.73%. The paper also discusses the contributions of the proposed method, including the introduction of a pioneering hybrid algorithm, an improved LightGBM model, and rigorous real-world validation. The results highlight the potential of the SAOA-LightGBM algorithm in enhancing fault warning systems, with applications in various sectors such as transportation, energy, and finance.