  With exponential growth in the human population, it is vital to conserve
natural resources without compromising on producing enough food to feed
everyone. Doing so can improve people's livelihoods, health, and ecosystems for
the present and future generations. Sustainable development, a paradigm of the
United Nations, is rooted in food, crop, livestock, forest, population, and
even the emission of gases. By understanding the overall usage of natural
resources in different countries in the past, it is possible to forecast the
demand in each country. The proposed solution consists of implementing a
machine learning system using a statistical regression model that can predict
the top k products that would endure a shortage in each country in a specific
period in the future. The prediction performance in terms of absolute error and
root mean square error show promising results due to its low errors. This
solution could help organizations and manufacturers understand the productivity
and sustainability needed to satisfy the global demand.
