Groundwater NO3 contamination (GNC) threatens the drinkability of water in many countries worldwide. It could cause serious health problems and sometimes lead to death. This paper aims to introduce a comprehensive approach that combines GIS, statistics, and Machine Learning (ML) for groundwater quality managementincluding both water quality assessment and prediction. The performances of this approach are discussed through its application in assessing and predicting nitrate (NO3) concentrations in the Eocene Aquifer, Palestine. Spatiotemporal records of NO3 over the period 1982–2019 are integrated with a database and used in this research. The database includes the following factors: well depth, well use, anthro-pogenic on-ground activities, watersheds, soil type, and land use. Geo-statistical assessment using GIS and statistical boxplot is employed to assess the variability of NO3 concentrationsand how they are affected by the independent indicators. Assessment outcomes (NO3 distribution and the influencing factors) were used to build the Random Forest (RF) prediction model. Such a model is used to predict GNC levels in groundwater based on multi-influencing factors. Assessment results indicate increasing and decreasing trends of GNC in the southern and middle parts of the study area, respectively. It also provides the RF model by the main inf luencing factors affecting GNC in the study area which are: well depth, well use, anthropogenic on-ground activities, watersheds, and land use. Results indicate that RF has an average and maximum prediction accuracy of 88.5 and 91.7%, respectively. The well groundwater depth has the highest influence on GNC. This research could support water authority decision-makers toward the adoption of sustainable groundwater protection plans in Palestine.