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Food security is one of the most pressing global challenges, exacerbated by climate change, political instability, and economic fluctuations. This review explores the potential of artificial intelligence (AI) in enhancing food security and mitigating the impacts of climate change. By leveraging advanced algorithms, machine learning techniques, and data analytics, AI can enhance food production, distribution, and consumption. Predictive models powered by AI enable precise climate forecasts and adaptive strategies, offer unprecedented efficiency in agricultural planning. This review investigates AI’s role in reshaping food security, focusing on applications such as predictive modelling, resource optimization and environmental impact assessment. AI methods, including Regression Models, Time Series Forecasting, Neural Networks, and Clustering Techniques, are used to improve agricultural productivity, crop quality, yield, and resilience to climate-related disruptions. Furthermore, these technologies assist governments and policymakers with actionable climate insights, supporting efforts to mitigate the impacts of extreme climate events. While the benefits of AI in addressing food security challenges are significant, there are several challenges. These include data privacy concerns, high implementation costs, and the need for adequate infrastructure and digital literacy. AI’s potential in encouraging a sustainable and secure food future is based on addressing these challenges.
Bhairo et al. (Wed,) studied this question.