Leveraging Machine Learning for Hunger Alleviation and Food Security Solutions

Abstract

In the face of global food insecurity, machine learning (ML) is emerging as a transformative tool to address hunger alleviation. By harnessing the power of data analytics, machine learning models can predict crop yields, optimize agricultural practices, and monitor food supply chains in real-time. These predictive algorithms provide valuable insights for farmers and policymakers, enabling them to make informed decisions on planting strategies, resource allocation, and food distribution. Furthermore, ML can assist in detecting patterns of food scarcity and predicting areas at risk of hunger, thereby facilitating timely interventions and preventing widespread food shortages.

DOI - https://doi.org/10.65525/SVUP.9788199963351.2026.172-180