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Remote sensing through satellites and internet of things (IoT) technology are two widespread techniques to assess inland water quality. However, both these techniques have their limitations. IoT provides point data, which is insufficient to represent entire water body, especially if the water body has complex terrain and hydrology. Through remote sensing, we can sample data of a large area, but data acquisition is constrained by satellite. Revisit time and quality of estimates can be affected by image resolution. Moreover, non-optical properties that might affect water quality cannot be sensed through satellites. To complement this, GIS data from labs can be useful for providing higher resolution and accurate data and can be used as ground truth. Thus, in this chapter, the authors aim to integrate both these data collection techniques followed by estimation and prediction through machine learning models. The accumulated datasets are used to train machine learning (ML) models deployed at a server. The selected ML model is an artificial neural network with train accuracy of 97% and test accuracy of 95%.