aspg-21-3753-1791475899 20261008161139 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Neutrosophic Science IJNS 2692-6148 2690-6805 2025 26 3 N. Metawa University of Sharjah, UAE; Tashkent State University of Economics, Uzbekistan Sait Revda Dinibutun College of Business Administration, American University of the Middle East, Kuwait Maha Saad Metawea College of business administration, Delta University for Science and Technology, Egypt The idea of neutrosophic set (NS) from a philosophical viewpoint is a generality of the theory of indeterminacy FS (IFS) and fuzzy set (FS). A NS is considered by a falsity, a truth and indeterminacy membership functions and all membership amount is an actual standard or a non-standard sub-set of the non-standard unit interval ]−0, 1+[. E-commerce is successful for the growth of novel business methods and should be constantly improved in the numerous decades. According to the growing E-commerce, supply chain management (SCM) has been strongly affected as we are now previously overcome by achievement in either developed or developing economies. Nowadays, E-commerce in advanced economy characterizes the newest lead of possibility in physical distribution systems and SCM, even if it emerging economy, e-commerce market is even in its infancy however, it is increasing and become integral part of commercial life. This paper presents a Quadripartitioned Neutrosophic Pythagorean Soft Set-Based Prediction Model for Supply Chain Management (QNPSSPM-SCM) model Using Hybrid Optimization Algorithms. The proposed QNPSSPM-SCM technique is for presenting an advanced E-commerce in SCM using advanced optimization techniques. At first, the min-max normalization method has been applied in the data pre-processing stage to convert input data into a beneficial pattern. In addition, the presented QNPSSPM-SCM system executes quadripartitioned neutrosophic Pythagorean soft set (QNPSS) technique for the prediction process. At last, the hybrid grey wolf optimization and teaching-learning-based optimization (GWO‐TLBO) algorithm fine-tunes the hyperparameter values of the QNPSS model optimally and results in better performance of prediction. The experimental validation of the QNPSSPM-SCM method is verified on a benchmark database and the outcomes are determined regarding different measures. The experimental outcome underlined the development of the QNPSSPM-SCM method in prediction process. 2025 01 13 3753 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJNS.260301 https://www.americaspg.com/journal/21/article/3753 https://www.americaspg.com/storage/91744131123.pdf