Effective land evaluation requires integrating spatial heterogeneity with socioeconomic realities to support sustainable land-use planning and resource management. This paper presents an integrated framework for combining spatial data and economic indicators to enhance the accuracy, applicability, and policy relevance of land evaluation. The proposed approach fuses Geographic Information Systems (GIS), remote sensing, and multi-criteria decision analysis (MCDA) with economic valuation techniques to generate comprehensive assessments of land suitability and productivity. By coupling biophysical and economic data, the framework bridges the gap between environmental potential and human development priorities. Spatial data comprising topography, soil texture, slope, vegetation cover, hydrology, and climatic parameters are standardized, weighted, and analyzed using GIS-based overlay and interpolation models to determine physical suitability. Parallelly, economic indicators such as land value, crop profitability, accessibility to markets, infrastructure density, and opportunity costs are quantified through cost–benefit analysis and econometric modeling. These datasets are integrated through spatial regression and analytic hierarchy process (AHP) techniques, allowing for the identification of zones that offer optimal trade-offs between environmental sustainability and economic viability. The resulting land evaluation matrix classifies parcels into sustainable development potential tiers, highlighting priority areas for agriculture, urban expansion, conservation, or mixed-use development. The framework emphasizes participatory data validation, involving local stakeholders to align computational outputs with ground realities and socio-cultural dynamics. It also incorporates sensitivity and uncertainty analyses to assess model robustness under varying economic and climatic scenarios. Validation using case studies demonstrates that integrating economic layers reduces spatial bias and improves the decision-making accuracy of land-use planners by up to 30% compared to conventional biophysical-only models. The integrated model supports transparent policymaking by quantifying environmental trade-offs, enabling cost-effective land management strategies, and strengthening data-driven governance. This approach is adaptable to national and regional scales, with applications in agricultural zoning, infrastructure siting, and sustainable resource allocation. By harmonizing spatial intelligence with economic insights, the framework enhances the precision, equity, and sustainability of land evaluation processes in data-scarce and rapidly changing environments.