This research explores the novel use of the partial least squares regression (PLSR) as an alternative model to the conventional least squares (LS) model for modeling tide levels. The modeling is based on twenty tidal constituents: M2, S2, N2, K1, O1, MO3, MK3, MN4, M4, SN4, MS4, 2MN6, M6, 2MS6, S4, SK3, 2MK5, 2SM6, 3MK7, and M8. The 1st, 2nd, and 3rd PLSR components are selected from 40 PLSR components for the modeling based on the computed variances, Yloadings, and Yscores. The PLSR results are compared with those of the LS. The normality of the model residuals are evaluated by the Jarque–Bera statistical test. The computed probabilities of the normality test for 1st, 2nd, and 3rd PLSR components and LS are p = 0.0611, p = 0.0656, p = 0.916, and p = 0.0517, respectively, which all indicate p > 0.05, and imply that the residuals are normally distributed. The nature of tide criterion is verified by computing the tidal form factor F. The computed tidal form factor for the 1st, 2nd, and 3rd PLSR components and LS are F = 0.1794, F = 0.1696, F = 0.1599, and F = 0.1848 respectively. All the models satisfy the semidiurnal criterion of 0 ≤ F ≤ 0.25 at the 95% confidence level, since the observed tide is characteristically semidiurnal. The computed coefficient of determination for the 1st, 2nd, and 3rd PLSR components and LS are r2 = 0.9134, r2 = 0.9825, r2 = 0.9933, and r2 = 0.7861 respectively. These results prove that the PLSR model outperformed the conventional LS model, and therefore, a viable alternative to the conventional LS model.