Brain tumor segmentation in magnetic resonance imaging (MRI) plays a pivotal role in early diagnosis and treatment planning. Various segmentation techniques have been proposed, with texture analysis emerging as a promising method for extracting meaningful features from MRI images. This research paper presents a comprehensive comparison of existing brain tumor segmentation techniques that utilize texture analysis. The study covers a range of state-of-the-art methods, including statistical, model-based, and deep learning approaches, evaluating their performance in terms of accuracy, sensitivity, specificity, and computational efficiency. We explore the impact of different texture features, such as gray-level co-occurrence matrix (GLCM), Gray-level run-length matrix (GLRLM), and local binary pattern (LBP), on segmentation results. Additionally, the research investigates the robustness of these techniques across diverse MRI datasets and tumor types, considering factors such as image resolution, noise, and tumor heterogeneity. The experimental evaluations are conducted on benchmark datasets, and the results are analysed comprehensively to provide insights into the strengths and limitations of each approach. This research aims to guide researchers and practitioners in selecting appropriate texture-based segmentation methods based on specific clinical requirements and imaging conditions.