This paper proposes an innovative hybrid algorithm combining Simulated Annealing (SA), Arithmetic Optimization Algorithm (AOA), and new search strategies to optimize the hyperparameters of LightGBM for industrial fault warning. The hybrid algorithm is integrated with LightGBM to form a sophisticated fault warning system. Industrial case studies demonstrate that the proposed algorithm outperforms advanced methods in prediction accuracy and generalization ability. In a real-world water pump application, the algorithm achieved a fault warning accuracy rate of 90%. Compared to three advanced algorithms—Improved Social Engineering Optimizer-Backpropagation Network (ISEO-BP), Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN), and Grey Wolf Optimizer-Light Gradient Boosting Machine (GWO-LightGBM)—its Root Mean Square Error (RMSE) decreased by 7.14%, 17.84%, and 13.16%, respectively, while its R-Squared value increased by 2.15%, 7.02%, and 3.73%, respectively. The method also shows a leading success rate in water pump fault warning. The hybrid algorithm improves the performance of LightGBM by combining chaotic mapping and dynamic reverse learning strategies to generate excellent initial solutions, and by adopting a nonlinear inertia weight factor and an improved search strategy to enhance the robustness and accuracy of the AOA algorithm. Simulated Annealing is then applied for global search, further improving the algorithm’s performance. The proposed method is validated using real industrial system data, demonstrating significant performance advantages in fault warning. Compared to other state-of-the-art methods, the proposed method not only exhibits higher accuracy and reliability but also provides new insights for fault warning research in the industrial domain. The paper makes three significant contributions: (1) introducing a pioneering hybrid algorithm combining SA and AOA, (2) proposing an improved LightGBM model specifically designed for fault warning tasks, and (3) conducting rigorous real-world industrial case validations to substantiate the effectiveness of the proposed methods. The hybrid algorithm is applied to a water pump case study, where it successfully detected faults with high accuracy. The algorithm's performance is evaluated using RMSE, R-Squared, and Relative Percentage Deviation (RPD) metrics. The results show that the proposed method outperforms other algorithms in fault warning accuracy and generalization ability. The proposed hybrid algorithm is compared with ISEO-BP, LSTM-CNN, and GWO-LightGBM. The results demonstrate that the SAOA algorithm achieves optimal values in both RMSE and R-Squared metrics compared to other state-of-the-art algorithms, proving its superiority. The 95% confidence interval analysis further demonstrates the stability and robustness of the SAOA algorithm. The models trained by each algorithm are applied to the fault warning test, further validating the performance of SAOA in practical applications.This paper proposes an innovative hybrid algorithm combining Simulated Annealing (SA), Arithmetic Optimization Algorithm (AOA), and new search strategies to optimize the hyperparameters of LightGBM for industrial fault warning. The hybrid algorithm is integrated with LightGBM to form a sophisticated fault warning system. Industrial case studies demonstrate that the proposed algorithm outperforms advanced methods in prediction accuracy and generalization ability. In a real-world water pump application, the algorithm achieved a fault warning accuracy rate of 90%. Compared to three advanced algorithms—Improved Social Engineering Optimizer-Backpropagation Network (ISEO-BP), Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN), and Grey Wolf Optimizer-Light Gradient Boosting Machine (GWO-LightGBM)—its Root Mean Square Error (RMSE) decreased by 7.14%, 17.84%, and 13.16%, respectively, while its R-Squared value increased by 2.15%, 7.02%, and 3.73%, respectively. The method also shows a leading success rate in water pump fault warning. The hybrid algorithm improves the performance of LightGBM by combining chaotic mapping and dynamic reverse learning strategies to generate excellent initial solutions, and by adopting a nonlinear inertia weight factor and an improved search strategy to enhance the robustness and accuracy of the AOA algorithm. Simulated Annealing is then applied for global search, further improving the algorithm’s performance. The proposed method is validated using real industrial system data, demonstrating significant performance advantages in fault warning. Compared to other state-of-the-art methods, the proposed method not only exhibits higher accuracy and reliability but also provides new insights for fault warning research in the industrial domain. The paper makes three significant contributions: (1) introducing a pioneering hybrid algorithm combining SA and AOA, (2) proposing an improved LightGBM model specifically designed for fault warning tasks, and (3) conducting rigorous real-world industrial case validations to substantiate the effectiveness of the proposed methods. The hybrid algorithm is applied to a water pump case study, where it successfully detected faults with high accuracy. The algorithm's performance is evaluated using RMSE, R-Squared, and Relative Percentage Deviation (RPD) metrics. The results show that the proposed method outperforms other algorithms in fault warning accuracy and generalization ability. The proposed hybrid algorithm is compared with ISEO-BP, LSTM-CNN, and GWO-LightGBM. The results demonstrate that the SAOA algorithm achieves optimal values in both RMSE and R-Squared metrics compared to other state-of-the-art algorithms, proving its superiority. The 95% confidence interval analysis further demonstrates the stability and robustness of the SAOA algorithm. The models trained by each algorithm are applied to the fault warning test, further validating the performance of SAOA in practical applications.