Many studies have shown that economic growth is first dependent on increases in agricultural productivity, especially in developing countries. The agriculture sector of a country is instrumental in the early development of a sustainable economy as it helps to provide an economically viable food source for its population. This economic growth facilitates the creation and expansion of a middle class which experts agree is instrumental in creating social equity and improving the quality of life for a country’s citizens. Leveraging a machine learning model, our web application will provide open access to a crop optimization tool allowing individuals, small businesses, and governments to make well-informed decisions about which crops are best suited to a particular area and climate. We combined crop yield data with environmental factors such as temperature, rainfall, pesticides, and location data to start building a predictive machine learning model. With customizable user input, this model could be used to provide informative predictions concerning crop yield optimization to assist in tackling food insecurities in developing countries.