This review discusses strategies for designing functional microbial synthetic communities (SynComs) to better understand the roles of microbes in plant growth and health. SynComs are simplified microbial communities that allow researchers to study the molecular and chemical basis of specific microbiome functions. The review highlights the importance of integrating high-throughput experimental assays with computational genomic analyses to tailor SynComs based on functional traits rather than just taxonomic identities or co-occurrence patterns. The design of SynComs involves selecting microbial strains that represent the taxonomic and functional characteristics of a microbiome under study. This can be done by using taxonomic profiles such as high abundance across samples, co-occurrence with other community members, or differential abundance between samples with contrasting phenotypes. Additionally, the review emphasizes the use of functional traits, such as biosynthetic potential, to guide SynCom design. Various strategies for designing SynComs are discussed, including taxonomy-based design, functional trait-based design, and the use of computational models to predict microbial interactions. The review also highlights the importance of integrating different types of 'omics data and experimental data to provide a more accurate depiction of microbial diversity, dynamics, and functions. The review also discusses the use of artificial intelligence and machine learning to optimize SynCom design by navigating the highly dimensional combinatorial space of taxa and functions. These approaches can help identify microbial strains that are predictive of phenotypic outcomes and improve the experimental design. The review concludes that the design of SynComs is no longer solely based on taxonomy but increasingly involves selecting microbiome members that show positive or negative interactions, possess specific functional traits, or have complementary/similar niche preferences. This multifaceted approach can enhance SynCom functionality, enabling tailored designs with increased resilience. The integration of computational and experimental methods is essential for the successful design and validation of SynComs.This review discusses strategies for designing functional microbial synthetic communities (SynComs) to better understand the roles of microbes in plant growth and health. SynComs are simplified microbial communities that allow researchers to study the molecular and chemical basis of specific microbiome functions. The review highlights the importance of integrating high-throughput experimental assays with computational genomic analyses to tailor SynComs based on functional traits rather than just taxonomic identities or co-occurrence patterns. The design of SynComs involves selecting microbial strains that represent the taxonomic and functional characteristics of a microbiome under study. This can be done by using taxonomic profiles such as high abundance across samples, co-occurrence with other community members, or differential abundance between samples with contrasting phenotypes. Additionally, the review emphasizes the use of functional traits, such as biosynthetic potential, to guide SynCom design. Various strategies for designing SynComs are discussed, including taxonomy-based design, functional trait-based design, and the use of computational models to predict microbial interactions. The review also highlights the importance of integrating different types of 'omics data and experimental data to provide a more accurate depiction of microbial diversity, dynamics, and functions. The review also discusses the use of artificial intelligence and machine learning to optimize SynCom design by navigating the highly dimensional combinatorial space of taxa and functions. These approaches can help identify microbial strains that are predictive of phenotypic outcomes and improve the experimental design. The review concludes that the design of SynComs is no longer solely based on taxonomy but increasingly involves selecting microbiome members that show positive or negative interactions, possess specific functional traits, or have complementary/similar niche preferences. This multifaceted approach can enhance SynCom functionality, enabling tailored designs with increased resilience. The integration of computational and experimental methods is essential for the successful design and validation of SynComs.