There are remarkable molecular and embryological similarities in cardiogenesis between Drosophila and vertebrates. Cells comprising the Drosophila heart can be subdivided into individual identities based on differences in morphology, function and gene expression patterns. Recent studies have shown that differential modifications of histone proteins, in vivo transcription factor (TF) binding, and the presence of particular TF binding motifs can be used as predictive signatures of the enhancers that govern cell-specific gene expression. Here we used discriminative training methods within an integrative, multi-species framework to uncover the motifs, enhancers and genes underlying cardiac cell fate decisions. As an initial step, we undertook a large-scale validation of Drosophila heart enhancers, which revealed enhancer activities in distinct subpopulations of cardiac cells. To identify related cell-specific regulatory elements, we used the validated enhancers as a training set in a machine learning approach that integrated TF motifs with ChIP data for both TF binding and histone modifications. Empirical validation of candidate enhancers predicted by this method confirmed activity in the appropriate cardiac cells. By clustering the motifs derived from the individual cardiac classifiers, we identified and validated sequence features which discriminate specific cellular identities. Next, we asked if similar predictive signatures underlie mouse and human cardiomyocyte (CM) differentiation from embryonic stem cells (ESCs). We show that the distribution of histone marks found within differentiating human and mouse ESCs indeed predict genes potentially critical for CM differentiation, with the best predictions provided by the overlapping mouse and human candidates. We evaluated this result in a large-scale RNAi-based screen of Drosophila orthologs of the mammalian genes, which uncovered dozens of novel cardiogenic regulators whose function is being tested in differentiating human ESCs. In total, these results document the utility of computational modeling combined with empirical testing to uncover the enhancers, TF motifs and genes which characterize individual cardiac cell fates in both invertebrate and mammalian species.