Bioinformatics is a critical interdisciplinary domain that combines computational techniques with the analysis of biological data. Structural bioinformatics primarily examines the biomolecular structures, such as proteins and nucleic acids as well as their interactions. The rice of high-throughput sequencing and big data analytics has rendered bioinformatics tools increasingly vital in structural biology, facilitating the comprehension of molecular functions, drug development and evolutionary relationships. This chapter examines various computational tools that enable structural analysis, including homology modeling, molecular docking, molecular dynamics (MD) simulations and protein-protein interaction. Furthermore, it highlights recent progress in artificial intelligence (AI), machine learning (ML), and deep learning (DL), which are revolutionizing conventional operations through enhanced feature extraction and predictive modeling. Additionally, the chapter emphasizes the significance of quantum computing and high-performance computing (HPC) in improving simulation accuracy and scalability. Collectively, these advancements are transforming the bioinformatics domain, offering robust solutions for the analysis of complex biological systems and expediting biomedical research.