The Nador Lagoon (Marchica), a sensitive coastal ecosystem in northeastern Morocco, is increasingly affected by anthropogenic pressures such as urban development, wastewater discharge, and intensive agriculture. This study proposes the integration of artificial intelligence (AI) tools into a real-time water quality monitoring system to enhance pollution detection, analyze degradation factors, and support data-driven governance. Based on physicochemical parameters collected across 16 stations, two modeling approaches-Multiple Linear Regression (MLR) and a simplified Artificial Neural Network (ANN)-were evaluated. The results demonstrated that even basic predictive models could achieve reasonable accuracy and serve as valuable tools for environmental monitoring when integrated with visual analytics platforms like Power BI. The study lays the groundwork for developing more advanced AI-driven environmental monitoring solutions.