Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3431 2019 2019 Cybersecurity Department, Faculty of Science and Information Technology, Jadara University, Irbid, Jordan Qusay Qusay Cybersecurity Department, College of Computer Sciences and Informatics, Amman Arab University, Amman, Jordan Qusay Bsoul Faculty of Information Technology, Applied Science Private University, Amman, Jordan Fadi yassin Salem Al jawazneh Department of Clinical Pharmacy Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan Raghad W. Bsoul MEU Research Unit, Middle East University, Amman, Jordan; Jadara Research Center, Jadara University, Irbid, Jordan Diaa Salama AbdElminaam Computer Science Department, Alobour high institute for comHigh Instituteputer and informatics, Cairo, Egypt Magdy Abd Abd-Elghany Faculty of Computer Sciences, Misr International University, Cairo, Egypt Yasmin Alkady Computer Science Department, Alobour high institute for comHigh Instituteputer and informatics, Cairo, Egypt Ibrahim A. Gomaa 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 2025 01 16 10.54216/JISIoT.150101 https://www.americaspg.com/articleinfo/18/show/3431