<p>The microbial constituents of rhizosphere vary from one geolocation to another as multiple factors including climate conditions, soil characteristics and plant species modulate the microbial diversity and abundance. They influence growth and survival of different plant species under normal and stressed conditions along with their physiology and development. Extensive efforts have been undertaken in last few decades to comprehend the microbiome of the many rhizopspheric samples. Initially, such work relied upon cultural techniques and methodologies. At present, metagenomics, metatranscriptomics and associated techniques like metaproteomics has revolutionised the microbiome analysis. The advent of long-read sequencing technologies such as Oxford Nanopore and PacBio sequencing has significantly improved metagenomic assembly accuracy. Generation of data in trillions of base pairs have led to application of advanced computing and algorithms. Therefore, a discussion and critical analysis of advancements in microbiome research, particularly regarding applications in agricultural and environmental microbiology, are imperative and desired. Prior knowledge of microbiome emerging from plant-soil-microbe interaction equip us to formulate effective strategy for the application of beneficial microbes with potential plant growth promotion and biocontrol activity. The application of deep learning and machine learning, leveraging the vast availability of metagenomics data to construct and analyze microbial networks will be highly effective in identifying the core microbiome of niche ecosystems, including keystone species. Additionally, machine learning (ML) and deep learning (DL) algorithms, including Random Forests, Support Vector Machines (SVM), and convolutional neural networks (CNNs), now play a crucial role in microbial classification and predicting plant-microbe interactions. Advancement of associated tools and techniques will provide a better understanding of planet’s environment cycles via assessment of microbial richness and their seasonal fluctuation. High-throughput microbial culturomics using AI-driven automation and robotics has enabled the culturing of previously unculturable microbes, revolutionizing microbiome studies. These cutting-edge approaches are reshaping rhizosphere research thus, facilitating sustainable agricultural applications. In this context, recent progress of all major tools and methodologies utilised for microbiome assessment have been analysed and discussed.</p>