The textile industry holds a central position in India's economy, contributing substantially to both employment and GDP. Despite technological advancements, maintaining stringent quality standards remains a persistent challenge due to defects such as cracks, stains, and inconsistencies in fabrics. Traditional manual inspection methods, while effective to a degree, are labor-intensive, time-consuming, and prone to human error. This paper proposes an innovative approach to address these challenges through the application of machine learning and computer vision techniques in fabric defect detection. Specifically, the research focuses on integrating advanced texture feature extraction methods—Gray-Level Co-occurrence Matrix (GLCM), Local Binary Patterns (LBP), and Histogram of Oriented Gradients (HOG)—with a robust classification framework using Bayesian optimized Random Forest. The methodology emphasizes efficient feature selection via Minimum Redundancy Maximum Relevance (MRMR), enhancing the system's accuracy and efficiency. By leveraging a comprehensive dataset from Kaggle encompassing diverse fabric defects, the proposed system aims to significantly improve defect detection accuracy, reduce manual intervention, and ensure consistent product quality across textile manufacturing processes. The highest accuracy achieved in the evaluation is 99.52%.