Groundwater quality assessment is essential for sustainable water resource management in semi-arid regions, where increasing anthropogenic pressures and natural hydrogeochemical processes significantly influence groundwater composition. The Khemisset–Tiflet aquifer, located in northwestern Morocco, constitutes a major source of water for domestic and agricultural uses but remains vulnerable to quality degradation. This study aims to characterize the physicochemical properties of groundwater and investigate the dominant factors controlling its hydrochemical variability through an integrated and reproducible analytical framework. A total of 34 groundwater samples were collected during the May 2018 field campaign. In situ measurements included pH, temperature, electrical conductivity (EC), total dissolved solids (TDS), salinity, dissolved oxygen, and depth to groundwater level. Complementary laboratory analyses of selected major and trace elements were performed for seven highly mineralized sampling stations using ICP-AES. Spatial variability was investigated using Geographic Information Systems (GIS), while multivariate statistical analysis was performed through Principal Component Analysis (PCA) implemented in Python using open-source scientific libraries. The results revealed pronounced spatial heterogeneity in groundwater mineralization, with highly mineralized groundwater occurring in specific sectors of the aquifer. Electrical conductivity ranged from 2 to 8408 µS cm⁻¹, while TDS varied between 1 and 5633 mg L⁻¹. PCA highlighted strong associations among EC, TDS, and salinity, reflecting their common mineralization signal, while a secondary dimension was mainly associated with temperature and depth to groundwater level. The observed spatial patterns are consistent with the combined influence of groundwater circulation and water–rock interaction, although the relative contribution of natural and anthropogenic processes cannot be established from the available dataset. The integrated use of field measurements, GIS, and Python-based multivariate analysis provides a transparent framework for characterizing groundwater-quality variability and establishes a baseline for future monitoring of the Khemisset–Tiflet aquifer.