aspg-3-3456-1791417745 20261008000225 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2025 18 1 Firas Zawaideh Cybersecurity Department, Faculty of Science and Information Technology, Jadara University, Irbid, Jordan Qusay Bsoul Cybersecurity Department, College of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan Ala Alzoubi Faculty of Information Technology, Applied Science Private University, Amman, Jordan Nardine T. Botros Faculty of Engineering , Misr International University , cairo , Egypt Moaz T. Fawzy Faculty of Engineering , Misr International University , cairo , Egypt Diaa Salama AbdElminaam MEU Research Unit, Middle East University, Amman, Jordan; Jadara Research Center, Jadara University, Irbid, Jordan Nour Mostafa College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait This paper presents an optimized framework for detecting SMS spam using advanced machine learning algorithms and natural language processing (NLP) techniques. Two datasets, the Filtering Mobile Phone Spam Dataset and the SMS Spam Collection Dataset, were utilized to evaluate the performance of various classifiers, including Multinomial Naive Bayes, K-Nearest Neighbors, Support Vector Classifier, Decision Trees, and AdaBoost. The methodology encompasses comprehensive data preprocessing steps, such as tokenization, stopword removal, and text normalization, followed by feature extraction using TF-IDF and Bag-of-Words models. The classifiers’ performances were evaluated using accuracy, precision, recall, and F1-score, alongside cross-validation techniques. Results indicate that Support Vector Classifier and AdaBoost consistently achieved superior accuracy in distinguishing between spam and ham messages. The study underscores the importance of data preprocessing and model optimization in enhancing spam detection accuracy, offering valuable insights for improving SMS filtering systems in cybersecurity applications. 2025 145 182 3456 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.180112 https://www.americaspg.com/journal/3/article/3456 https://www.americaspg.com/storage/31735530946.pdf