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Their increasing variability, driven by environmental and process constraints, raises multiple questions regarding the relationship between fuel composition, safety, and performance. In this context, predictive models can help support fuel formulation while reducing experimental effort. Quantitative structure\u2013property relationship (QSPR) methods have been applied to fuel components such as hydrocarbons to predict various properties, showing promising results but often with limited accuracy and applicability. Several studies have also investigated mixture\u2010QSPR (M\u2010QSPR) approaches for hydrocarbon systems involving constituents also present in fuels; however, very few studies directly address real fuel mixtures. This is because M\u2010QSPR remains particularly challenging due to mixture interactions, data scarcity, representation difficulties, and validation issues. 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