The convergence of plant genomics and pharmaceutical chemistry, empowered by bioinformatics, has significantly accelerated the discovery and development of phytochemicals with therapeutic potential (Gauthier et al., 2018). This chapter explores the computational strategies that have revolutionized phytochemical drug discovery, focusing on the integration of multi-omics data, virtual screening, molecular docking, chemoinformatics, and machine learning. Advances in plant genomics, particularly through next-generation sequencing, have unveiled biosynthetic gene clusters responsible for the production of bioactive secondary metabolites. Transcriptomic analyses further refine this understanding by revealing gene expression patterns associated with phytochemical biosynthesis (Can, 2013). Computational tools such as AutoDock and Glide facilitate virtual screening and molecular docking, enabling researchers to predict binding affinities and interactions between plant-derived compounds and biological targets. Molecular dynamics simulations provide additional insight into the stability and behavior of these complexes. Quantitative Structure-Activity Relationship (QSAR) modeling supports the rational optimization of phytochemicals based on structural features to enhance efficacy and minimize toxicity. Integrating genomics, transcriptomics, proteomics, and metabolomics through platforms like MetaboAnalyst offers a systems biology approach to understand and manipulate metabolic pathways. Meanwhile, machine learning techniques—such as support vector machines and neural networks—enhance predictive capabilities for biological activity, pharmacokinetics, and toxicity profiling of phytochemicals. In summary, computational approaches have transformed phytochemical research, making the drug discovery process more systematic, efficient, and scalable. The synergy between plant genomics and pharmaceutical chemistry, mediated by bioinformatics, continues to unlock new avenues in therapeutic development (Johnson, 2007).