In-situ measurements and physico-chemical analyzes of thirty (30) samples taken bimonthly from August 2015 to December 2016 on six (6) stations of Lake M'koa were carried out. A modeling study was made in order to determine a quantitative and qualitative relationship between chlorophyll-a and five physico-chemical descriptors (temperature, turbidity, oxidative power (RH), nitrate ions (NO3-) and nitrite (NO2-)). These descriptors constituted the explanatory and predictive parameters of chlorophyll-a of samples taken from Lake M'koa. This study was carried out by using Principal Component Analysis (PCA), Ascending Hierarchical Classification (AHC), Multiple Linear Regression (RML) and Nonlinear (RMNL) methods. Two quantitative and qualitative linear and nonlinear models (RML and RMNL) have been proposed. These accredited models as good statistical indicators have been validated according to the rules established by the Organization of Economic Cooperation and Development (OECD). Statistical indicators of RMNL reveal more efficient predictions with R2 = 0.942, RMSE = 0.049 and F = 291.986. The obtained results suggest that the combination of these five descriptors could be useful in predicting the property of chlorophyll-a. In addition, turbidity is the first most important descriptor for the prediction of chlorophyll-a at the M'koa Lake different stations.