Authors : Sonia Nain; Rashi Datten

Volume/Issue : Volume 11 - 2026, Issue 8 - August

Google Scholar : https://tinyurl.com/2vc4z97e

Scribd : https://tinyurl.com/4bdm72t4

DOI : https://doi.org/10.38124/ijisrt/26aug027

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Global food security is increasingly challenged by climate change, rapid population growth, declining agricultural resources, and the emergence of new plant diseases, necessitating innovative and sustainable agricultural solutions. Molecular biology has transformed crop science through advances in genomics, gene editing, molecular diagnostics, and functional genomics, enabling a deeper understanding of plant traits and stress responses. Simultaneously, Artificial Intelligence (AI) has emerged as a powerful computational tool capable of processing complex biological datasets, predicting crop performance, identifying disease patterns, and supporting precision agricultural decision-making. This review explores the integration of molecular biology and AI as a unified approach to enhancing sustainable food security. It examines how machine learning, deep learning, computer vision, and bioinformatics complement molecular techniques to accelerate crop improvement, optimize resource utilization, improve disease surveillance, and strengthen climate-resilient agricultural systems. The paper further discusses recent advancements, practical applications, existing challenges, ethical considerations, and future research opportunities associated with AI driven molecular agriculture. In addition, a conceptual AI–Molecular Biology Integration Framework is proposed to illustrate the systematic interaction between biological data acquisition, intelligent analytics, predictive modelling, and sustainable agricultural implementation. The review highlights that combining molecular biology with AI has the potential to improve productivity, resource efficiency, and environmental sustainability while supporting resilient food production systems. This interdisciplinary approach offers valuable insights for researchers, policymakers, and agricultural stakeholders seeking to develop intelligent and sustainable strategies for future global food security.

Keywords : Sustainable Food Security, Molecular Biology, Artificial Intelligence, Machine Learning, Genomics, Bioinformatics, Precision Agriculture, Crop Improvement, Climate-Resilient Agriculture, Intelligent Farming.

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