The blend of mathematical modeling with process data affords both meaningful qualitative and quantitative interpretation of outcomes which may advance our knowledge of microbial physiology and even suggest newer developments in fermentation technology. The study focused on the formulation of a generalized framework for modeling two-dimensional inhibitions where both substrate and product inhibitions simultaneously affect the microorganisms. A substrate-product inhibition geometry was constructed to simultaneously reflect the effect of substrate and product inhibitions during ethanol fermentation. Nine inhibition equations were formulated from the inhibition geometry. All statistical inferences showed that sudden stop substrate-linear product (SSS-LP) inhibition was fitted best with sorghum extract data (alpha = 0.4437), exponential substrate-sudden stop product (ES-SSP) inhibition was fitted best with maize extract data (alpha = 2.0370), and sudden stop substrate-sudden stop product (SSS-SSP) inhibition best described cassava extract data (alpha = 1.8047). A noncompetitive inhibition was prevalent in all three inhibition models requiring a reduction in the concentration of the inhibitor to minimize the inhibition. The study was a major breakthrough to improving the designs of fermenters because it availed a library of inhibition kinetic models that are very reliable in the mitigation of inhibitions during ethanol fermentation especially when carefully chosen and properly applied.