This paper presents a new technique to predict the optimal amount of load to be shed at various loading conditions using Quantum-Inspired Evolutionary Programming–Support Vector Machine (QIEP-SVM). QIEP is utilised to optimise the RBF Kernel parameters in Least-Square Support Vector Machine (LS-SVM). The objective of the optimisation is to minimise the mean square error (MSE). The performance of QIEP-SVM technique was compared with those obtained from LS-SVM technique with prediction accuracy through a 10-fold cross-validation procedure. All simulations in this study were carried out using IEEE 69-bus distribution test system. QIEP-SVM model had shown better prediction performance as compared to LS-SVM. The results also indicate that the proposed approach outperforms the most recently reported technique in terms of accuracy and fast computation time.