Aquifer quality monitoring and scoping are essential for both groundwater control and water provision in agricultural areas. The Irrigation Geothermal Water Quality Index (IGWQI) was investigated based on 6 variables (EC, SAR, Cl, Na, HCO3, and T) for 41 water samples using conventional method and predicted with support vector machine (SVM) and classification tree and regression (CART) computer models as an indicator to evaluate the suitability of geothermal water for irrigation. It was found that the use of such parameters allows for more accurate identification of unsuitable groundwater areas and reduces estimation bias, particularly if the dataset is limited (6 variables). In this study, the SVM and CART models were developed and validated to assess the groundwater quality of the EL Hamma aquifer. The values predicted by the SVM and CART approaches are in extremely good agreement with the measured IGWQI, with a mean coefficient of determination (R2) close to 1, about 0.91 and 0.99, respectively, thus ensuring practicality and confidence for the artificial intelligent model. Findings showed that three classes of groundwater quality were identified: 12% are good, 56% are satisfactory, and 32% are classified as unsuitable water. The poor water quality for irrigation is shown in the eastern parts of the study area, which is nearly similar to that of the water temperature, averaging between 35 and 57 °C. This rise in water temperature is controlled by the fault that connects the deep compartments of the system and the shallow aquifer. IGWQI delineation and mapping is an excellent tool for the protection and safeguarding of groundwater resources, notably in arid areas where groundwater is the sole source of life, as well as an important tool in decision-making for groundwater planning and use.