Soil microbes play a vital role in agricultural ecosystems due to their immense diversity and their complex interactions with crops. The precise characterization of beneficial and harmful microbes remains challenging form microcosms, primarily due to the vast diversity and large populations present in the soil and other related habitat. Owing to tackle such scenario, meta-transcriptomics offers a powerful and effective approach for analysing metagenomic mRNA, providing valuable insights into the regulation and expression profiles of complex microbial communities in diverse cohort and environmental samples. Given the multistep nature of the data processing involved in meta-transcriptomics, the development of robust pipelines and algorithms is essential to manage the complexity and scale of these datasets, ensuring accurate and reliable taxonomic classification, functional annotation, and expression analysis. Consequently, several pipelines and algorithms have been developed to streamline the analysis of metatranscriptomics data. This chapter reviews the commonly used computational pipelines and algorithms, with a particular focus on their applications in agriculture, specifically in enhancing crop productivity and supporting sustainability. Routine applications of these technologies in agriculture include deciphering the microbial community structure and function of agricultural soils, identifying novel microbiota responsible for bioremediation of polluted soils, diagnosing plant diseases of economic significance, and assessing postharvest food quality and nutrient cycling—ultimately contributing to the enhancement of crop productivity and sustainable agricultural practices.