Monitoring the quality of air, water, and soil, tasks that in the recent past were manageable using well-structured machine learning (ML) techniques. In air quality monitoring, ML algorithms are used to analyse different datasets that contain, for example, pollutant level concentrations, meteorological reports, and geospatial characteristics. Similarly, water quality monitoring used ML methods on large and complex datasets that include those types of information as well as parameters such as PH level, dissolved oxygen level, and pollutant concentrations. In stark contrast, soil quality management used machine learning algorithms for soil property estimates, nutrient levels, and hazard identification. ML in combination with sensor technologies, remote sensing data, and IoT devices (Siris & Shakya 2022) create environmental monitoring systems quicker and allow them to be more efficient while maintaining the same level of accuracy for proactive public health and sustainable ecosystems from real-time decisions.