Biofilms are complex, structured microbial communities encased within an extracellular matrix, adhering to surfaces in diverse environments. Their formation and persistence provide significant challenges in healthcare, industry, and environmental management. Traditional microbiological methods provide critical insights into biofilms, but they often fall short in unraveling the complexity of their multiscale interactions. Systems biology and computational systems biology have emerged as transformative approaches to study biofilms by integrating experimental and computational methodologies. This chapter explores the application of multi-omics technologies, such as genomics, transcriptomics, proteomics, and metabolomics, in deciphering the molecular mechanisms underlying biofilm formation, structure, and resistance. Computational approaches, including agent-based models, network-based models, and continuum models, are discussed for their roles in simulating biofilm behavior and predicting dynamics. The integration of experimental data with computational frameworks has facilitated real-time modeling, hybrid approaches, and predictive simulations, enhancing our understanding of biofilm systems. Practical applications of these insights are highlighted, including the development of anti-biofilm strategies targeting quorum sensing and matrix components, optimizing industrial processes, such as wastewater treatment, and engineering biofilms for customized functionalities. Although challenges, such as biofilm heterogeneity and the scalability of computational models, remain, advancements in data integration and artificial intelligence promise to address these limitations. In conclusion, systems biology and computational tools have revolutionized biofilm research, bridging fundamental knowledge with real-world applications. This interdisciplinary approach provides a comprehensive understanding of biofilms, paving the way for innovative solutions in medicine, industry, and environmental science.