Texture classification plays a significant role in computer vision that affects many fields, including medical image analysis, content-based image retrieval, face recognition, industrial inspection, etc. The effectiveness of texture classification relies on the extensiveness of the features extracted from the image data. Despite advancements in texture classification, many existing methods still struggle with ambiguous pattern representation and insufficient integration of local and global texture features, leading to decreased classification performance on complex datasets. To address these challenges, in this paper, we proposed a novel framework for texture classification by integrating rule-based motifs with magnitude textons. The method begins by transforming the input image into a complete magnitude-based texton-indexed (CMTi) image by examining the local pixel intensity relationships on a 2x2 grid, which precisely encapsulates structural features. Further, it applies an average filter to the 2x2 grids of the CMTi image, then calculates the absolute difference between each pixel of the CMTi image and the average of the 2x2 grid. Later, on the image derived average rule-based motif (ARMiCMT) indexed image through predefined rules, ensuring consistent and unique motif indexing even in cases of ambiguous intensity values, it is named as the average rule-based motif on complete magnitude texton (ARMiCMT) indexed image. Subsequently, the Gray Level Co-occurrence Matrix (GLCM) is computed on the ARMiCMT indexed image at various angles. This operation yields six spatial features: energy, contrast, entropy, angular second moment, correlation, and homogeneity. The feature vector integrates local descriptors with global spatial relationships, resulting in a holistic representation of texture. This strong feature extraction method enhances accuracy and robustness in texture classification, making it highly effective for diverse applications.