Brain tumors have recently become a widespread phenomenon that affects almost all age ranges. Magnetic resonance imaging (MRI) is a widely used technique nowadays to diagnose this life‐threatening disease. Therefore, using a computer‐aided diagnosis system is essential for the early identification and classification of brain tumors. This work aims to develop a fully automated classification framework for MRI brain tumor images using machine learning (ML) algorithms. In this article, four ML algorithms such as support vector machines (Cubic SVM), k‐nearest neighbors (fine KNN), decision tree (fine tree), and naive Bayes (Kernel) were implemented with two feature extraction approaches: histogram of oriented gradients (HOG) and a bag of features (BOF) approach. The novelty of this work is based on the original usage of BOF and HOG approaches over different types of ML algorithms for MRI brain tumor image classification. In addition, combining two public datasets that contain three brain tumor types to generalize and improve the proposed work. The results showed that the fine KNN algorithm achieved the greatest success among all algorithms using the HOG method with an accuracy of 98.62% on 6328 MRI images.