The revolution of high-resolution imaging and large-scale dataset has led to new avenues of remote sensing, medical imaging, and multimedia domains. Traditional methods of feature extraction and matching have their own challenges concerning scalability, efficiency, and robustness when handling large datasets. Thus, the study offers a novel feature extraction and enhancement framework, with the help of classical computer vision techniques, including neural network-based enhancements to resolve these challenges. Using robust key point detection and description, based on Harris Corner Detection, Laplacian of Gaussian (LoG), and Hessian-based segmentation, the descriptors combine Scale Invariant Feature Transform (SIFT) and Oriented FAST and Rotated BRIEF (ORB) descriptions to provide holistic feature representation with refining using a customized neural network and further matching is performed using the descriptors obtained with FLANN-based matcher with a ratio test for further accuracy. Experimental results show that the framework improves feature detection, enhances matching robustness, and achieves scalability for high-resolution and large-volume data processing, which provides a basis for advanced applications in image analysis.