The accurate classification of gliomas from magnetic resonance (MR) imaging is vital for effective treatment planning. However, due to the irregular and diffuse boundaries of gliomas, manual classification is both difficult and time-consuming. To address these challenges, we present a novel methodology that combines image decomposition with local texture feature extraction to improve classification accuracy. The process begins by applying a Gaussian filter (GF) to the MR images to smooth them and reduce noise. Subsequently, Non-subsampled Laplacian Pyramid (NSLP) decomposition is used to capture multi-scale image details, which enhances the visibility of glioma boundaries. After decomposition, Total Variation-L1 (TV-L1) normalization is applied to reduce intensity inconsistencies, and Local Binary Patterns (LBPs) are utilized to extract key texture features from the processed images. These extracted texture features are then input into several supervised machine learning classifiers, including Support Vector Machines (SVMs), K-nearest Neighbors (KNNs), Decision Trees (DTs), AdaBoost, and LogitBoost. These models are trained to distinguish between low-grade (LG) and high-grade (HG) gliomas. Experimental results demonstrate that our proposed method consistently outperforms current state-of-the-art techniques in glioma classification, delivering superior accuracy in differentiating between LG and HG gliomas. This approach offers significant potential for improving diagnostic accuracy, thereby supporting clinicians in making informed and effective treatment decisions.