The landscape of drug discovery and development has transformed significantly, driven by advancements in computational techniques. In particular, structure-based drug design (SBDD), a subset of computer-aided drug design (CADD), has emerged as a crucial tool in early-stage drug discovery. Traditional approaches often focusing on a single target face challenges in addressing complex multifactorial diseases and drug resistance. This has led to the evolution of multi-target drug design (MTDD), a paradigm employing a general perspective. This chapter explores the rationale for MTDD emphasizing the advantages and limitations of this approach. Unlike conventional “one target-one drug” strategies, MTDD aims to develop single drugs that simultaneously target multiple proteins or pathways involved in a disease. The computational methods in MTDD integrate principles from computational biology, cheminformatics, structural bioinformatics, and machine learning to expedite drug discovery. This interdisciplinary approach enables the systematic analysis of complex biological networks, identification of key nodes and interactions, and prediction of the impact of potential drug candidates on multiple targets. The application of MTDD spans various areas in drug discovery leveraging knowledge from complete genomes and proteomes. The chapter also discusses the potential of MTDD for various diseases and provides a comprehensive overview of MTDD, addressing its rationale, computational techniques, and successful applications in treating complex diseases. The emergence of computational approaches in MTDD represents an innovative and promising approach in the dynamic field of drug discovery.