The combination of the Internet of Things (IoT) and machine learning (ML) in smart agriculture is changing the way agriculture is practiced in the modern world because of increased levels of efficiency, production, and sustainability. IoT equipment, like crops’ moisture content sensors, weather temperature and humidity sensors, drones, and smart irrigation systems, collect information in real time about various environmental parameters like soil moisture, temperature, humidity, and the health of the crops. This information is then sent to the various central organizations for further data analysis. IoT devices contribute to the generation of a lot of data and that data is subjected to machine learning algorithms to generate inferences. ML models solve problems such as forecasting yield, early detection of crop diseases, refraining watering the crops in an already expected rainfall period, even forecasting weather for optimum treatment application among many other decisions made by farmers. For instance, ML models can forecast how seasons with certain weathers affects the growth of a certain crop; however, the models may also be extrapolated to prevent outbreak of certain diseases incase sensor health monitoring of a certain crop indicates thresholds close to ill or sick cry. Geographic Information Systems and remote sensing allow for the realization of precision agriculture, a farming practice whereby the sound application of resources in the use of water, fertilizers, and pesticides is observed. Smart agriculture is also a solution to the global issues of food security and resources because it increases food production without increasing the amounts of resources input. With improved technology, smart agriculture is rapidly changing the mode and ways of farming, thus sufficing the need for feeding a growing global population in a more sustainable way.