With growing water scarcity, treated paper mill effluent (TPME) is increasingly reused in agriculture for its water and nutrient value. However, long-term use raises concerns about pollutant buildup in soil and water, potentially harming ecosystems and human health. This review explores how machine learning (ML) models can help predict and manage the environmental impacts of TPME over time. The objective is to assess the effectiveness of various ML techniques such as decision trees, neural networks and ensemble models in forecasting changes in soil and water quality due to TPME irrigation. We reviewed recent studies, datasets and real-world applications to evaluate the performance and limitations of these models. Findings show that ML offers clear advantages over traditional models by handling complex, non-linear data patterns and improving prediction accuracy. However, challenges remain, including data availability, quality and the complexity of model interpretation. This review highlights the potential of ML as a powerful decision-support tool for sustainable wastewater management. It also emphasizes the need for better data practices and collaboration between environmental scientists, policymakers and technologists. By integrating ML into regulatory frameworks, the paper industry can move toward safer, more sustainable effluent reuse. In bridging technology with environmental science, this study supports the adoption of ML driven solutions to enhance long term environmental monitoring and promote greener practices in industrial water management.
