The Upper Cretaceous Austin Chalk (AC) Group is an unconventional reservoir that extends across Texas and Louisiana. It is composed of interbedded layers of marly chalks to calcareous-siliciclastic mudrocks that vary in the degree of lamination, bioturbation, mineral abundance, and organic matter richness. Integrating lithologic observations with geochemistry is critical for interpreting depositional environments and modeling reservoir properties. Central to this integration is the ability to characterize the geochemistry of core samples at a resolution that captures thin–layered heterogeneity common to mudrock systems. Here, we developed a training data set using a semisupervised chemofacies clustering approach that is explored with a deep neural network model to predict chemofacies across multiple cores of the AC Group. Eight chemofacies are identified that capture differences in inorganic geochemistry, mineral abundance, rock fabric, and organic matter richness; three classify differences in the marly chalks, four classify differences in the calcareous-siliciclastic mudrocks, and one is transitional between marly chalk and calcareous-siliciclastic mudrocks. Two distinct siliciclastic-carbonate mixing trends are identified that differ in modal abundances of tectosilicates and total clay. Two chemofacies are distinguished based on differences in Mo and V trace element enrichment, suggesting differences in bottom-water redox chemistry. Collectively, this approach provides a means to integrate geochemical measurements and lithological observations to interpret the depositional environments of mudrock systems and is an important step toward upscaling core data to characterize reservoir quality.