The fusion of the Internet of Things (IoT) with sensor technology is essential for transforming global agriculture, nurturing heightened sustainability and productivity. A multitude of technical, commercial, and ecological challenges present in this sector can be addressed through innovations in information and communication technology (ICT), the captivating domain of wireless sensor networks (WSN), and the enthralling universe of the Internet of Things. The expanding Itb of interconnected devices generates substantial amounts of big data, revealing varied modalities and temporal-spatial patterns. Efficient data processing and analysis are vital for developing sophisticated knowledge repositories and insights that enhance forecasting, decision making, and sensor management. This study offers an in-depth investigation of diverse machine learning techniques utilized in the analysis of sensor data within the agricultural sector. Moreover, it showcases an intriguing case study of a prototype that adeptly intertwines IoT with a data-driven methodology for a food, energy, and water (FEW) system.