The formulation of advanced metalworking fluids (MWFs) relies heavily on the precise selection and integration of organic friction modifiers to optimize the tribological performance of machining operations. This paper investigates the complex structure-property relationships governing organic friction modifiers, focusing on their chemical architecture and subsequent boundary film formation across diverse metallurgical substrates. By synthesizing insights from experimental tribology and advanced data-driven modeling techniques, this study proposes a comprehensive, hypothetical framework designed to evaluate and predict the frictional behavior of various fluid formulations on distinct metal surfaces. The structural components of the modifiers, notably their polar anchoring groups and non-polar aliphatic chains, are analyzed in the context of their competitive adsorption and reaction dynamics. Ultimately, this research bridges the gap between empirical friction studies and autonomous, machine-learning-driven materials discovery, offering a predictive methodology to tailor MWFs for specific ferrous and non-ferrous applications while mitigating traditional trial-and-error bottlenecks.