aspg-18-3431-1792419403 20261019141643 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Journal of Intelligent Systems and Internet of Things JISIoT 2769-786X 2690-6791 2025 15 1 Hassan Al Sukhni 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 Fadi yassin Salem Al jawazneh Faculty of Information Technology, Applied Science Private University, Amman, Jordan Raghad W. Bsoul Department of Clinical Pharmacy Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan Diaa Salama AbdElminaam MEU Research Unit, Middle East University, Amman, Jordan; Jadara Research Center, Jadara University, Irbid, Jordan Magdy Abd-Elghany Computer Science Department, Alobour high institute for comHigh Instituteputer and informatics, Cairo, Egypt Yasmin Alkady Faculty of Computer Sciences, Misr International University, Cairo, Egypt Ibrahim A. Gomaa Computer Science Department, Alobour high institute for comHigh Instituteputer and informatics, Cairo, Egypt Accurate detection and classification of brain tumors are essential for timely diagnosis and effective treatment planning. This study presents an integrated framework leveraging both machine learning (ML) and deep learning (DL) models for brain tumor detection and classification using MRI images. Two publicly available datasets are utilized: one for binary classification (tumor vs. no tumor) and another for multiclass classification (glioma, meningioma, and pituitary tumors). Comprehensive preprocessing steps, including resizing, feature extraction using the Gray Level Co-occurrence Matrix (GLCM), and feature selection via Chi-square testing, were employed to optimize the dataset for modeling. Machine learning models such as Decision Trees, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and AdaBoost were compared with deep learning architectures like Convolutional Neural Networks (CNNs) and the pre-trained VGG16 model. Hyperparameter optimization techniques, including grid search and the Adam optimizer, were used to enhance model performance. The models were evaluated using metrics such as accuracy, precision, recall, F1-score, Mean Squared Error (MSE), and Mean Absolute Error (MAE). Results indicate that the VGG16 model consistently outperformed other approaches, achieving high validation accuracy. This study highlights the potential of integrating ML and DL techniques for accurate and efficient brain tumor detection and classification, offering valuable tools for medical diagnostics. 2025 01 16 3431 https://creativecommons.org/licenses/by/4.0/ 10.54216/JISIoT.150101 https://www.americaspg.com/journal/18/article/3431 https://www.americaspg.com/storage/81735355350.pdf