Water quality in delta ecosystems is shaped by factors like climate change, which raises temperatures and triggers harmful algal blooms, and anthropogenic activities such as agriculture and pollution that introduce heavy metals and nutrients. Additionally, natural disasters, geological factors, and tipping points related to sea level rise and flooding further contribute to water degradation. Traditional methods lack the temporal and spatial coverage required for effective management, making remote sensing (RS) technologies increasingly valuable for retrieving water quality parameters (WQPs) in real time. Recent advancements, for example, optical and hyperspectral sensors and artificial intelligence (AI), have improved the accuracy of quality of water measurement by enabling diverse retrieval methodologies. These include empirical, analytical, semi-empirical, and machine-learning algorithms, all of which leverage various data sources like satellites and in situ sensors. Automation in water quality systems, incorporating continuous data acquisition and transmission, facilitates near real-time monitoring, generating big data for predictive modeling. This is particularly important in river deltas, where climate change and human activity intensify water quality issues. RS applications offer decision-makers powerful tools for spatiotemporal WQP analysis, supporting the development of successful guidelines and management procedures. The combination of advanced RS technologies along with traditional methods is crucial for addressing water quality challenges and fostering sustainable river delta ecosystem management in the face of environmental change.