Artificial intelligence (AI) is continuing to gain traction as technology improves and there is increased data collection. AI can assist society by predicting future phenomena and thus this leads to more efficient processes, increased profits, and lower costs. The evolving world environment is leading to increased pressure for food, energy, and water resources and it becomes increasingly important to manage these resources. AI can be utilized in the form of the internet of things (IoT), which are sensors and devices that collect data, to help manage natural resources. In this paper, several machine learning models are employed to predict crop yield. Crop yield is an important factor for many countries and can be an indicator of food security. The ability to predict crop yield is of increased importance as it can aid policymakers in planning future food supply and can aid in the development of a smart irrigation system. A case study of sugarcane in the state of Florida is utilized, along with climate and soil variables. As a follow-up to the results of the machine learning models on crop prediction, this paper introduces an optimization model that develops the smart irrigation system. The model chooses the optimal amount of irrigation and fertilizer needed to maximize crop yield while also minimizing costs, water, and energy resources. The machine learning models identify the most important variables needed for crop prediction and can be applied to other crops in other regions of the world.