<![CDATA[Drinking water quality monitoring is essential to ensure compliance with water quality standards and protect public health. However, conventional monitoring methods often relied on periodic inspections and laboratory testing, making continuous supervision difficult. This study aimed to develop an Internet of Things-based drinking water quality monitoring system integrated with machine learning to classify water quality and predict the remaining filter lifetime. The system utilized pH, turbidity, and Total Dissolved Solids sensors connected to an ESP32 microcontroller for real-time data acquisition. Measurement data were transmitted, stored in a MySQL database, and processed using Support Vector Machine for water quality classification and Support Vector Regression for filter lifetime prediction. Machine learning models were evaluated using metrics for classification and regression, while sensor performance was tested by comparing measurement results with reference equipment. The results show that the developed sensors achieve average error values of 3.94% for pH, 5.26% for turbidity, and 1.82% for Total Dissolved Solids measurements. The Support Vector Machine model achieves an accuracy of 94.85% in classifying water quality, while the Support Vector Regression model obtains low prediction errors and a coefficient of determination of 0.9977. The integrated system successfully performs real-time monitoring, water quality classification, and filter lifetime prediction. These findings indicate that the proposed system provides an effective approach for drinking water quality monitoring and predictive filter maintenance.]]>
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