This study assessed the water quality status and identified the most influential parameters using data from 16 physicochemical indicators measured at three sampling stations over a one-year period. Spatial and temporal variations were first examined, followed by evaluation of relationships among variables using Spearman correlation analysis. The results revealed that EC, TDS, Mg²⁺, SO₄²⁻, Cl⁻, Na⁺, and alkalinity exhibited similar temporal patterns and strong correlations, suggesting a common hydrogeochemical origin associated with salinity and mineral dissolution. Nitrate and total nitrogen also showed similar trends, indicating the influence of agricultural runoff and fertilizer inputs. To reduce multicollinearity, principal component analysis (PCA) was applied, and three principal components explained a substantial proportion of total variance. The dominant component was mainly associated with salinity and dissolved salts, with TDS as the central factor. Subsequently, a stepwise multivariate regression model was developed to determine the most significant predictors of dissolved heavy metals. The model demonstrated good predictive performance for arsenic (R² = 0.662) and barium (R² = 0.652). Overall, the findings indicate that regional water quality is governed by both natural geochemical processes and anthropogenic activities, particularly agriculture. The integrated application of trend analysis, correlation, PCA, and regression provides a comprehensive framework for interpreting water quality variations and supports effective reservoir management and pollution control strategies.