The study centers on the simulation of the spatio-temporal differences of groundwater standard in different geological formations in Imo state, using suitable iterative modeling. For a period of twelve (12) months, seventy two groundwater samples were collected from six hydrological formations of Imo State. The obtained values were analyzed for twenty-two different physiochemical parameters. The considered zones were the Benin Formation (BF), Ogwashi Asaba Formation (OAF), Nsukka Formation (NF), Alluvium Formation (AF), Imo Clay Shale Formation (ICSF), and False Bedded Sandstones Formation (FBSF). The average concentrations of total dissolved solids, chloride, nitrate, sulphate, total hardness and electric conductivity were higher in the dry season compared to the rainy season, while average concentrations potassium and bicarbonate were higher in wet season. The water quality index (WQI) was evaluated in accordance with WHO permissible standards for safe drinking water on a scale of 0 to 100. The WQI for dry season were 50.10, 24.98, 20.18, 35.79, 79.77 and 55.94 for BF, OAF, NF, AF, ICSF, and FBSF respectively while for rainy season, the WQI gotten were 35.04, 73.30, 27.54, 30.37, 86.98 and 108.95 for BF, OAF, NF, AF, ICSF and FBSF respectively. The results reveal that during dry season, groundwater samples from OAF and NF have excellent water quality, samples from BF, NF, and AF have good quality water and samples from ICSF have very poor water quality. The WQI obtained during the rainy season indicate that water samples from BF, NF and AF have good water quality for drinking and agricultural applications based on national and international indices and standards while the water samples from OAF were of poor water quality. The water standard from ICSF shows very poor quality and the water quality from FBSF is unsuitable for drinking purpose. This suggests that there is need for continuous monitoring and treatment for acidic and high nitrate water to mitigate future pollution and ensure sustainable use of the groundwater resource. The modeling was done for several iterations using XG boost model and after several iterations the dataset and a value decreased.