Determination of the anions and cations in groundwater samples is of great importance in evaluating the chemical and physical properties of water in the urban and rural environments. To this end, these hydrochemical signatures are measured using analytical techniques such as ion chromatography (for anions), atomic absorption spectroscopy, or inductively coupled plasma methods (for cations). These methods involve collecting and filtering samples, preserving them appropriately, and analyzing them with high-precision instruments to quantify the ionic composition. Therefore, this study examines a short path in the determination of major ions; Na+, Ca+, K+, Mg2+, HCO3−, Cl−, and SO42− via basic tests of pH, electrical conductivity (EC), and total dissolved solids (TDS). For this, groundwater quality sample data from 12 observation wells around Bursa (Türkiye) from 2015 to 2018, recorded every six months were used. Then, the pH, EC, and TDS are used as inputs of two alternative models of random forest (RF; a single-output model executed seven times for seven outputs), and multi-output random forest (MORF; a novel algorithm that predicts seven outputs at the same time). Results indicated a good concordance between observation and predicted values, while the MORF (maxR2: 0.961; min RMSE: 0.136; minMAE: 0.050) results were superior to RF (maxR2: 0.886; min RMSE: 0.262; minMAE:0.124), and yet all of the outputs were predicted simultaneously. Further assessment of the scatter and the box-plots showed that the MORF can actively be used to quickly determine the hydrochemical signatures in the groundwater environment.