Phytoplankton represents one of the most important biological components of primary production, trophic interactions, and circulation of organic matter in lakes and reservoirs. To contribute to the understanding of eutrophication processes and ecological status of the small, shallow Butoniga reservoir, a machine learning tool for induction of models in form of decision trees and rule-based models was applied on a dataset comprising physical, chemical, and biological variables measured at four stations. Two types of models were successfully elaborated, i.e., (1) model describing phytoplankton Phylum, which describes and connects phytoplankton Phylum with phytoplankton abundance and biomass, and (2) model simulating phytoplankton biomass according to environmental variables which could be used in management purposes. Such models and their presentation contribute to a better understanding of the Butoniga reservoir ecosystem functioning.