Transcriptional regulation is the major regulatory mechanism that controls the spatial and temporal expression of genes during development. This is carried out by transcription factors (TFs), which recognize and bind to their cognate binding sites. Recent studies suggest a modular organization of TF-binding sites, in which clusters of transcription-factor binding sites cooperate in the regulation of downstream gene expression. In this study, we report our computational identification and experimental verification of muscle-specific cis-regulatory modules in Caenorhabditis elegans. We first identified a set of motifs that are correlated with muscle-specific gene expression. We then predicted muscle-specific regulatory modules based on clusters of those motifs with characteristics similar to a collection of well-studied modules in other species. The method correctly identifies 88% of the experimentally characterized modules with a positive predictive value of at least 65%. The prediction accuracy of muscle-specific expression on an independent test set is highly significant (P<0.0001). We performed in vivo experimental tests of 12 predicted modules, and 10 of those drive muscle-specific gene expression. These results suggest that our method is highly accurate in identifying functional sequences important for muscle-specific gene expression and is a valuable tool for guiding experimental designs.