Support Vector Regression (SVR) has already been proved to be one of the most referred and used machine learning technique in various fields. In this study, we have addressed a predictive-cum-prescriptive analysis for finalizing fund allocations by the Government at center to the schemes under Central Plan and to the schemes under States and Union Territories Plan, with a goal to maximize Gross Value Added (GVA) at factor cost. Here, we have proposed a hybrid machine learning model comprising of OFS (Orthogonal Forward Selection), TLBO (Teaching Learning Based Optimization) and SVR for the prediction of GVA at factor cost. In this model, referred as OFS–TLBO–SVR hybrid model, SVR is at the core of prediction mechanism, OFS is for identifying the relevant features, and TLBO is to support in optimizing the free parameters of SVR and again TLBO is used for optimizing the governable attributes of data.