A study on the quality control data from a large-scale dairy farm located under hot weather conditions was conducted. Physicochemical properties, microbial count data, and environmental variables (i.e., mean temperature and relative humidity) were examined. The analyses performed were Spearman's rank correlation, principal components analysis (PCA), and partial least squares regression (PLS). The correlation analysis revealed individual correlations between similar variables but weak between physicochemical properties and microbial counts. PCA identified low structure within the dataset but interestingly some seasonal patterns. The predictive modelling approach performed through PLS aimed to predict microbial counts, fat, and protein content using the physicochemical and environmental variables. Microbial counts were not well predicted, while the PLS model satisfactorily predicted fat and protein contents. These two physicochemical properties are associated with delivery payments for raw milk. This study characterized and identified relationships between the properties of raw milk. The utility of the statistical tools was demonstrated in understanding the quality control data. The results highlighted the need to consider data beyond the values regularly monitored in raw milk quality. Ultimately, these results can aid decision-making to improve raw milk quality.