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This article presents a dataset generated for a techno-economic assessment (TEA) of the methanol-to-jet (MtJ) fuel production pathway. The dataset was produced using a large-scale Monte Carlo (MC) sampling approach applied to a steady-state process model implemented in Aspen Plus V14. The techno-economic evaluation was conducted using an external cost model, with subsequent data processing performed in Python. In total, three million individual data points were generated by varying key technical and economic input parameters within predefined ranges and are under public access. For each MC sample, the net production cost on a mass basis (NPCm, EUR kgjet-fuel-1) of synthetic jet fuel was calculated as the primary economic performance indicator. The dataset comprises both the sampled input parameters and the corresponding techno-economic output variables and is intended to support transparency, reproducibility, and further uncertainty analysis of MtJ fuel production pathways.