Seasonal variations in the hydrochemistry of shallow groundwater can be due to the interactions between internal geochemical processes and external factors such as rainfall and human activities. This study applied seasonal and multivariate statistical analyses to understand the hydrochemical controls on shallow groundwater. The study area was divided into three sub-basins, or clusters (i.e., Birnin kebbi, Sokoto and Gusau). Fifteen shallow groundwater samples were derived from each cluster, totalling ninety shallow groundwater samples for dry and wet seasons. Physical parameters, including Temperature, Electrical Conductivity (EC), Total Dissolved Solids (TDS), Dissolved Oxygen (DO), and pH, were analysed in situ using handheld metres. However, chemical parameters (Ca2+, Mg2+, Na+, K+, Fe3+, Cu2+, Zn2+, CO3-, HCO3-, Cl-, SO42-, PO43-, NH3 and NO3-) were analysed in the laboratory. Subsequently, statistics were applied to study the impact of seasonality and groundwater evolution. Results of the Kruskal-Wallis test revealed that seasonality exerts a considerable influence on shallow groundwater through a significant difference in Temperature, EC, DO, TDS, HCO3-, Cl-, NH3 and PO43-. Pearson’s correlation analysis revealed strong relationships between hydrochemical elements, which suggest natural and anthropogenic influences on shallow groundwater evolution. Correlation results were concurrent with principal component analysis (PCA), hierarchical clustering analysis (HCA), and Piper and Gibbs models. Therefore, this study inferred that seasonality and rock weathering are the primary mechanisms controlling shallow aquifers' hydrochemistry in a semiarid Sokoto Basin. The seasonal and multivariate statistics provide a framework for more accurate shallow groundwater quality analysis while considering multiple groundwater quality parameters under different environmental conditions. It is hoped that the results of this study will inspire other researchers to use a similar method, especially those in semiarid environments. Seasonal and multivariate statistical analyses provide a user-friendly tool for monitoring shallow groundwater quality monitoring systems in global semiarid environments.