In recent years, eutrophication in the Paldang Lake has become one of the major environmental problems in Korea as it may threaten drinking water safety and human health. Thus it is important to understand the phenomena and predict the time and magnitude of algal blooms for applying adequate algal reduction measures. This study performed seasonal water quality assessment and chlorophyll-a prediction using Bayseian simple/multiple linear regression analysis. Bayseian regression analysis could be a useful tool to overcome limitations of conventional regression analysis. Also it can consider uncertainty in prediction by using posterior distribution. Generally, chlorophyll-a of a P2(Paldang Dam 2) site showed high concentration in spring and it was similar to that of P4(Paldang Dam 4) site. For the development of Bayseian model, we performed seasonal correlation. As a result, chlorophyll-a of a P2 site had a high correlation with P5(Paldang Dam 5) site in spring (r = 0.786, p<0.05) and with P4 in winter (r = 0.843, p<0.05). Based on the DIC (Deviance Information Criterion) value, critical explanatory variables of the best fitting Bayesian linear regression model were selected as a $PO_4-P$ (P2), Chlorophyll-a (P5) in spring, $NH_3-N$ (P2), Chlorophyll-a (P4), $NH_3-N$ (P4) in summer, DTP (P2), outflow (P2), TP (P3), TP (P4) fall, COD (P2), Chl-a (P4) and COD (P4) in winter. The results of chlorophyll-a prediction showed relatively high $R^2$ and low RMSE values in summer and winter.