Advancing effective and sustainable agricultural practices hinges on the integration of interactions between plants, soil, water, and pollutants into a cohesive model. The issues of water scarcity, soil degradation, and the infiltration of pollutants within the soil represent significant challenges that may affect plant and productivity. When the plant-soil-water-pollutant system is effectively modeled, it has the potential to enhance resource allocation, foster plant development, and alleviate the detrimental impacts of pollutants. In this manuscript, we examine the dynamics of plant-soil-water-pollutant interactions, underscoring the significance of ML algorithms in the representation of these intricate systems for intelligent agriculture. Our investigation is particularly aimed at understanding how data obtained from IoT sensors—such as soil moisture content, nutrient concentrations, pollutant levels, and assorted environmental conditions—can be applied to project plant growth, improve irrigation techniques, and evaluate soil health. We investigate into an array of ML methodologies, such as random forest, gradient boosting, and artificial neural networks, concerning their utility in estimating water demands, diagnosing soil health challenges, and anticipating plant distress resulting from pollutants. Moreover, this manuscript elucidates the obstacles associated with modeling such complex systems, encompassing data integrity, sensor calibration, and the nonlinear interrelations among soil, water, and pollutants. By integrating ML and IoT, this cohesive model holds the promise of transforming water management practices, augmenting crop yields, and fostering sustainable agricultural endeavors. To sum up, this work provides the importance of this unified structure in facing the dilemmas introduced by climate change, securing food supplies, and enhancing environmental sustainability.