Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/4106 2019 2019 Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt Ahmed Ahmed Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt Hala B. Nafea Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt; Faculty of Artificial Intelligence and Informatics, Horus University, New Damietta, 34517, Egypt Hossam El-Din Moustafa Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt; Applied Science Research Center, Applied Science Private University, Amman, Jordan El-Sayed M. El El-Kenawy Earthquake prediction is one of the most challenging problems in geophysical science, and conventional approaches have proven arduous in capturing the complexity and non-linearity of seismic measurements. The multidimensional nature of earthquake variability, along with class imbalance and the strong dependence of prediction results on hyperparameters, necessitates the development of more robust and flexible predictive models. In this paper, we introduce a bio-inspired ensemble learning method based on the Somersaulting Spider Optimizer (SSO) for dynamically adjusting classifier weights in earthquake classification. The proposed method addresses limitations of existing weighting strategies, which primarily focus on maximizing classifier contribution based on performance characteristics. Experiments were conducted on an earthquake dataset augmented with features modeled and mapped by time, space, and magnitude to capture patterns of seismic events. We compared the SSO-optimized ensemble with BaggingClassifier, CatBoost, HistGradientBoosting, LightGBM, and DecisionTree, as well as traditional ensemble approaches. Results show that the SSO-boosted ensemble achieved superior performance, with an accuracy of 97.01%, sensitivity of 97.04%, specificity of 99.36%, precision of 97.64%, and an F1-score of 97.33%, outperforming other models and traditional ensembles. These improvements were confirmed statistically using Wilcoxon signed-rank tests, while visual analyses demonstrated enhanced stability and generalization. Overall, the integration of bio-inspired optimization and ensemble learning shows strong potential to overcome challenges in earthquake forecasting and to support reliable early warning and disaster preparedness systems. 2026 2026 386 409 10.54216/JISIoT.180227 https://www.americaspg.com/articleinfo/18/show/4106