aspg-28-3859-1792156446 20261016131406 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Artificial Intelligence and Metaheuristics JAIM 2833-5597 2025 9 2 Khaled Sh. Gaber Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA Mahmoud Elshabrawy Mohamed Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA Accurate rainfall prediction supports resilient communities, sustainable agriculture, water distribution, and disaster preparedness. Because rainfall depends nonlinearly on interacting meteorological conditions, conventional forecast-ing methods can be inadequate for short-term, localized events. This study develops a machine-learning framework for next-day rainfall classification using daily weather observations from 20 major U.S. cities during 2024–2025. The dataset includes temperature, relative humidity, wind speed, precipitation, cloud cover, and pressure, together with a binary Rain Tomorrow label. Six classifiers are evaluated: Random Forest, Support Vector Machine (RBF kernel), k-Nearest Neighbors, Logistic Regression, Naive Bayes, and Linear SVM. SHapley Additive exPlanations (SHAP) are used to interpret model predictions and feature contributions. Random Forest reports the strongest results, with precision, recall, and F1-score of 1.00; RBF SVM and KNN achieve F1-scores of 0.97 and 0.94, respectively. Logistic Regression, Linear SVM, and Naive Bayes achieve F1-scores between 0.76 and 0.77. SHAP analysis identifies precipitation and humidity as the most influential variables, consistent with meteorological expectations. These results suggest that combining an ensemble classifier with interpretable explanations can support transparent rainfall forecasting for agriculture, urban planning, and emergency response. 2025 24 30 3859 https://creativecommons.org/licenses/by/4.0/ 10.54216/JAIM.090203 https://www.americaspg.com/journal/28/article/3859 https://www.americaspg.com/storage/articles/manuscripts/01M4Y5YS674Q791JV8W9EVJ2MZ.pdf