The carbon neutrality of existing internal combustion engines can be significantly enhanced through the use of sustainable e-fuels; thus, their price has to be reduced. Artificial intelligence (AI) offers a promising pathway to streamline and accelerate fuel development by enabling faster and more efficient model creation compared to conventional physicochemical simulations. Despite the apparent advantages, state-of-the-art research typically limits the application of AI to basic predictions within narrow operating ranges. This study introduces a novel AI-based fuel design tool capable of accurately predicting detailed engine performance across a broad range of operating conditions, using comprehensive physicochemical fuel properties as input. The proposed approach provides greater detail and precision than existing state-of-the-art methods. Building on a cost-efficient AI development strategy established in our previous work, the tool was constructed using 17 single-output multilayer perceptron networks. The tool was validated using engine dynamometer measurements with various test fuels, and then it was applied to a fuel optimization task to demonstrate its effectiveness. The results indicate that the tool’s predictions closely match actual engine performance. Specifically, 10 out of the 17 models achieved a mean absolute percentage error of <3 %. In the optimization scenario, the optimized fuel had a predicted engine operating score of 40.51 %, while the actual score was 41.3 %, demonstrating the tool’s potential for accurate fuel design. Thus, this novel approach can support the development of low-cost e-fuels, enabling economically viable, carbon-neutral mobility across a wide range of transport applications.