The circadian cycle orchestrates physiological and molecular processes through rhythmic gene expression. Uncovering relational patterns that link specific genes, experimental conditions, and time points is essential to understand the regulatory architecture of these rhythms. This study applies TriGen, a triclustering algorithm that identifies coherent gene-condition-time modules in circadian transcriptomic datasets. The analysis involved data preprocessing, tricluster extraction with TriGen, and functional enrichment analysis. TriGen uncovers biologically interpretable modules enriched in core clock components, as well as immune-related pathways, illustrating the potential of triclustering to reveal complex, condition-dependent circadian relationships. Across ten independent experiments, the method recovered up to seventy two percent of a curated set of literature-derived circadian genes, capturing both canonical clock regulators and auxiliary modulators involved in metabolism, immune signaling, and stress response. Functional enrichment analysis revealed significant associations with mitochondrial function, transcriptional regulation, neuronal signaling, and immune defense, reflecting the multifaceted systemic impact of circadian regulation. These results highlight the value of triclustering for dissecting high-dimensional temporal transcriptomic data and provide a framework to uncover novel regulatory modules that can guide targeted experimental validation.