Functional genomics aims to understand the dynamic aspects of gene expression and function at a systems level. Network biology offers a powerful framework to uncover functionally coherent gene modules by integrating various types of biological data. This review summarizes the current tools and computational techniques used for functional gene module discovery, highlighting their theoretical foundations, strengths, and limitations. We also discuss recent advances in integrating multi-omics data, single-cell analyses, and the application of machine learning. Emphasis is placed on the biological relevance and translational potential of identified gene modules in areas such as disease mechanism elucidation, biomarker discovery, and therapeutic target identification.