Brain tumors are abnormal growths of brain tissue that can develop into malignant tumors and severely reduce a person’s quality of life. Brain MRI scans can detect these tumors. To assist in identifying tumors, various computer-aided diagnostic techniques based on image processing have been developed. Early detection is critical for the diagnosis and treatment of brain tumors. Separating brain MRI images with and without tumors is a crucial step in this process. There are numerous classification methods available, such as Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Convolution Neural Network (CNN), Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), and others are used to distinguish between images of a normal brain and those with a tumor. This study proposes a method that involves using artificial neural networks (ANN) for categorizing and pre-processing brain images. MRI brain images texture characteristics are retrieved using the Gabor Transform and achieved an accuracy of 93.11%.