Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/3401 2019 2019 Cybersecurity Department, Faculty of Science and Information Technology, Jadara University, Irbid, Jordan Hassan Hassan Department of Computer Science ,Faculty of Computers and Artificial Intelligence , Benha University, Benha, Egypt Fatma Sakr Faculty of Information Technology, Applied Science Private University, Amman, Jordan Fadi yassin Salem Al jawazneh Department of MIS, College of Applied Studies and Community Service, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, Saudi Arabia Mutasem K. Alsmadi Computer Science Department, Alobour high institute for computer and informatics, Cairo, Egypt Ibrahim A. Gomaa Computer Science Department, Higher Technology Institute , 10th of Ramadan City, 12311, Egypt Shaimaa Abdallah Accurate weather forecasting is critical for sectors like agriculture, transportation, disaster management, and public safety. This paper presents a comprehensive methodology integrating traditional machine learning models, deep learning techniques, and ensemble learning approaches to enhance the precision and reliability of weather predictions. Using a combination of four datasets—two for classification and two for regression—the study evaluates various machine learning models such as Decision Trees, Support Vector Machines, and KNearest Neighbors, alongside ensemble methods like Bagging and AdaBoost. Additionally, deep learning models, particularly the Multilayer Perceptron (MLP), are employed to handle complex weather patterns. The Random Forest Regressor and Bagging Regressor consistently outperformed other models in terms of accuracy, precision, and F1-score. By comparing the performance of these models across different weather datasets, this research demonstrates the effectiveness of cross-validation and the importance of optimizing hyperparameters. The findings contribute valuable insights into enhancing the robustness and efficiency of weather forecasting systems, with potential applications in environmental monitoring, event planning, and climate change analysis.The findings indicate that Random Forest Regression consistently outperformed the other machine learning models evaluated. For ensemble learning, the Bagging Regressor was the top performer. In deep learning, the Multilayer Perceptron without cross-validation delivered outstanding performance. For the classification datasets, Random Forest achieved the highest accuracy, precision, and F-score. Our study also highlights the importance of cross-validation to prevent overfitting and ensure model robustness, as well as the impact of class imbalance on overall performance metrics. 2025 2025 260 284 10.54216/JCIM.150220 https://www.americaspg.com/articleinfo/2/show/3401