<p>Effective water quality monitoring (WQM) is critical for maintaining safe and sustainable freshwater resources, especially in the face of rising environmental challenges and increasing demands that elevate contamination risks. A comprehensive understanding of multidimensional water quality data is essential for assessing and predicting water status, identifying pollution sources, and protecting public health from waterborne diseases. This work reviews the main statistical techniques applied in WQM, emphasizing their foundational role in analyzing, simplifying, and interpreting water quality data, as well as enhancing their prediction. It provides an organizational structure for these techniques based on specific objectives, such as assessing spatio-temporal variations, detecting abrupt changes in water quality, and identifying underlying factors governing water pollution. This structure can assist water quality researchers and practitioners in selecting appropriate methods for their specific applications, considering desired outcomes and potential added value in WQM. This broad review underscores the ongoing relevance and significant contribution of statistical techniques in addressing water quality challenges, by improving management strategies, optimizing resource allocation, and enhancing public health protection. The potential of these techniques to complement other analytical methods in WQM and to enhance water quality forecasting is also highlighted.</p>