Uneven regional socioeconomic growth presents a fundamental economic challenge, as traditional budget allocation models frequently fall short by neglecting spatial topology and spillover effects. By striking a statistically correct balance between social equality and economic efficiency, this study seeks to maximize regional public funds. We present a new hybrid approach that uses Graph Attention Networks (GAT) in conjunction with multi-criteria optimization to model administrative districts as undirected graphs. By combining regularizations for temporal stability and spatial smoothness, the composite loss function scalarizes a logarithmic utility function for efficiency and a differentiable Gini index for equity. The GNN-based model removed major historical funding imbalances, reducing the highest regional funding gap from an 80-fold difference to a 1.08 ratio and reducing per capita funding variance by 92%, according to empirical testing conducted in Kazakhstan’s Almaty region. The system automatically reorganized 208.5 billion KZT (~463 million USD) at the ideal Pareto frontier (α = 0.5), allocating 96.4% of investments to the digitalization sector, which was given priority. According to the results, incorporating spatial topology, which explains 58.7% of choice variability, offers a highly scalable, empirically supported method for improving strategic public administration and reducing institutional stagnation.