Abstract: A method of estimating a level of one or more water parameters using trained machine learning (ML) based regression trees, comprising receiving physical features and spectral features relating to one or more sample of waters, propagating the water sample(s) through a regression trees comprising a plurality of nodes arranged in a plurality of branches propagating from a root node to a plurality of leaf nodes, each of the plurality of nodes is associated with an ML model trained to estimate a level of one or more water parameters in water samples, identifying a certain leaf node to which the water sample(s) propagated based on the plurality of physical features, estimating a level of one or more of the water parameters in the water sample(s) by applying the ML model associated with the certain leaf node to the spectral features, and initiating action(s) based on the water parameter(s) estimated level.