Two-sample \(U\)-statistics are widely used in a broad range of applications, including those in the fields of biostatistics and econometrics. In this paper, we establish sharp Cram\'er type moderate deviation theorems for Studentized two-sample \(U\)-statistics in a general framework, including the two-sample \(t\)-statistic and Studentized Mann-Whitney test statistic as prototypical examples. In particular, a refined moderate deviation theorem with second-order accuracy is established for the two-sample \(t\)-statistic. These results extend the applicability of the existing statistical methodologies from the one-sample \(t\)-statistic to more general nonlinear statistics. Applications to two-sample large-scale multiple testing problems with false discovery rate control and the regularized bootstrap method are also discussed.