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Assessing surface water quality for drinking use in developing countries is important since water quality is a fundamental aspect of surface water management. This study aims to improve surface water quality assessments and their controlling mechanisms using the drinking water quality index (<i>DWQI</i>) and four pollution indices (<i>PI</i>s), which are supported by multivariate statistical analyses, such as principal component analysis, partial least squares regression (PLSR), and stepwise multiple linear regression (SMLR). Twenty-two physicochemical parameters were analyzed using standard analytical methods for 55 surface water sites in the northern Nile Delta, Egypt. The <i>DWQI</i> results indicated that 33% of the tested samples represented good water, and 67% of samples indicated poor to unsuitable water for drinking use. The <i>PI</i> results revealed that surface water samples were strongly affected by Pb and Mn and were slightly affected by Fe and Cr. The SMLR models of the <i>DWQI</i> and <i>PI</i>s, which were based on all major ions and heavy metals, provided the best estimations with <i>R</i>² = 1 for the <i>DWQI</i> and <i>PI</i>s. In conclusion, integration between the <i>DWQI</i> and <i>PI</i>s is a valuable and applicable approach for the assessment of surface water quality, and the PLSR and SMLR models can be used through applications of chemometric techniques to evaluate the <i>DWQI</i> and <i>PI</i>s.