This research focuses on multi-attribute inventory classification and investigates the comparative effectiveness of multi-criteria decision making (MCDM) techniques and data mining. MCDM techniques like Simple Additive Weighting (SAW) and Analytic Hierarchy Process (AHP) have been used for ABC analysis and data mining algorithms namely Decision Tree and Support Vector Machine (SVM) algorithms are used to measure the classification accuracy of the employed MCDM techniques. The results depicts that the SAW based models outperform the AHP based models as SAW based decision tree achieved the accuracy of 84.84% and SAW based SVM achieved 80% accuracy. Conversely, the AHP based decision tree achieved 62% accuracy, while the AHP based SVM achieved 58.5% accuracy. These results show that the SAW technique may be preferred for accurate inventory classification. This research contributes to improving inventory management practices by providing insights into the effectiveness of MCDM techniques and data mining algorithms which may lead to improved decision making and optimized inventory control.