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Resource-dependent, spatially polarised, post-Soviet regional innovation systems remain under-studied, and are rarely analysed with the spatial-econometric and explaina-ble-machine-learning toolkit now standard in smart-city research. This paper uses an eleven-year official panel (2014-2025, 17 regions of Kazakhstan) covering R&D expendi-ture, innovation-active enterprises, the innovation-activity rate, innovative product output, and patenting. We triangulate five methods: Principal Component Analysis, Entropy Weight and TOPSIS integral indices; cluster analysis; panel econometrics; Random Forest and Gradient Boosting with SHAP; and spatial econometrics (Moran's I, Geary's c, LM diagnostics for SAR/SEM/SDM choice). We find extreme, persistent concentration of inno-vation inputs in Almaty and Astana (65.1% of national R&D expenditure, 2025), signifi-cant divergence on three of four indicators over 2014-2025, and a robust structural dis-connect between innovation inputs and outputs - a 'productivity-driven diffusion para-dox' - confirmed independently by correlation, factor analysis, panel regression, machine learning, and spatial econometrics. A robust three-tier regional typology is corroborated by an independent external study, and spatial analysis identifies peripheral-anomaly regions failing to absorb proximity benefits from more developed neighbours. We interpret these findings through Regional Innovation Systems, Smart Specialisation, New Economic Ge-ography, and Mission-Oriented Innovation Policy theory, and propose an evidence-based, AI-ready regional innovation governance framework - a unified data layer, explainable analytics, spillover corridors, and cluster-differentiated policy -transferable to other re-source-dependent, spatially polarised transition economies.