A local texton xor pattern (LTxXORP) is a new feature descriptor proposed for efficient content-based image retrieval in this paper. The suggested technique concentrates on capturing the spatial properties of images gathering texton XOR profiles. This process involves converting RGB color images into the HSV (hue, saturation, and value) color space. Then the V color space is cut into non-overlapping subblocks with dimension 2  \(\times \)  2 and textons are obtained by considering their shape. The last step is about an XOR action in the texton image between a center-pixel and its surrounding neighbors. In this paper, a new technique for speeding up content-based image retrieval (CBIR) systems using the parallel computing of local texton XOR pattern—powerful image texture descriptor is proposed. This approach includes splitting image areas into smaller segments and simultaneously computing localized texton XOR patterns in parallel, thus considerably reducing computation time while preserving reliable feature extraction. The results prove a remarkable increase in computation time without affecting the precision of the local texton XOR mark. The efficiency and scalability in performance are demonstrated through various performance evaluations and comparing it with other non-parallel methods. This makes it a viable option for use in real-time content-based image retrieval. Such an approach offers an additional avenue in which content-based image retrieval algorithms will become even faster.