From Rhizosphere to Resistance: Microbe‐Plant Interactions in Eco‐Smart Biocontrol

ABSTRACT The increasing limitations of chemical pesticides such as environmental pollution, pathogen resistance, and threats to human and ecosystem health have increased the demand for sustainable, biologically based crop protection methods. Eco‐smart biocontrol has emerged as a game‐changing paradigm that uses beneficial microorganisms associated with plants to suppress phytopathogens, boost plant immunity, and make agroecosystems more resilient over time. Moving beyond traditional single‐strain biocontrol, eco‐smart biocontrol integrates multi‐omics discovery, artificial intelligence‐assisted predictive microbiome design, and dynamic rhizosphere ecology. This review brings together ecological, molecular, and technological dimensions of eco‐smart biocontrol, focusing on the rhizosphere as a dynamic hotspot for plant–microbe interactions. We investigate rhizosphere microbiome assembly and demonstrate the preferential recruitment of beneficial bacteria, fungi, actinomycetes, and mycorrhizal symbionts by plant root exudates. Moreover, the review highlights the impact of innovations in multi‐omics techniques (metagenomics, transcriptomics, proteomics, and metabolomics), systems biology, and artificial intelligence on microbial biocontrol agent discovery, functional validation, and predictive design. Examples from cereal crops, legumes, and horticulture crops indicate that the application of beneficial microbial inoculants can significantly lower the burden of pests and diseases, enhance crop productivity, and fit perfectly within an integrated pest management system. Lastly, we critically analyze the main challenges preventing large‐scale adoption, such as inconsistent field performance, limited microbial survival and competitiveness, and comparative regulatory frameworks across global markets. Ultimately, eco‐smart microbial biocontrol combines mechanistic insights with omics‐driven discovery, artificial intelligence (AI)‐ assisted prediction, advanced formulation strategies, and field‐level validation, creating a strong, scalable, and environmentally friendly framework for resilient, low‐input agricultural systems.