Wastewater pollution is a major concern due to organic matter, pesticides, and other contaminants. Untreated discharge of this wastewater can pollute water resources and harm the environment. A data-driven approach for optimizing wastewater treatment systems and ensuring recycled water's safety and effectiveness by calculating energy, chemical, and greenhouse gas emissions. According to this study, the process of system optimization decreases the negative influence on the environment. This suggested research looks at the potential for reusing wastewater and purifying it so it can be used in coffee plants. A variety of methods for cleaning and disinfecting substances are detailed in the article. A wide range of physical, chemical, and biological processes can be utilized in these treatments. The primary objective of sewage wastewater treatment is to develop effective methods that ensure the safety and effectiveness of treated reused water for use in agriculture. data analysis using sensors Connected sensors that measure nutrients, pollutants, salinity, pH, organic matter, and toxins are being used to track various water quality measures. Fuzzy-based data processing utilizing FRNNs to handle uncertainties inherent in sensor data through fuzzy logic techniques. Recurrent neural networks capture temporal dependencies in the wastewater data, allowing for more accurate predictions. Compared with the other existing algorithms, the proposed method has the efficient treatment of wastewater and its safe reuse for coffee cultivation, promoting water conservation and sustainable agricultural practices.