The application of the Internet of Things (IoT) and machine learning in smart agriculture has brought about a new era of increased efficiency thanks to real-time data collection experienced and the use of advanced predictive analytics. The major difference of this study compared to others is that it is very specific because it is applying the Random Forest algorithm to improve decision-making in agriculture. The variety of IoT devices employed renders the collection of myriad data types in the form of environmental, soil, and operational metrics for which they are then analyzed for actionable insights. The designed system operates with ease, offering help in the tracking of real-time crop conditions, resource allocation, and crop production optimization. Nonetheless, by employing advanced software that is capable of executing data processing in an expeditious manner, the Random Forest algorithm guarantees the precise prediction of results and their consistent replicability. This plan is based on a logical method and, thus, it offers a multitude of answers to present-day challenges in agriculture, whereas farmers can adapt new data-driven techniques for environment-friendly agriculture.