In this study, the discharge coefficient (Cd) of piano key weirs was estimated using the Support Vector Machine (SVM) model, and its performance was subsequently compared with the Multilayer Perceptron Neural Network (MLPNN) model. For this purpose, the parameters of the upstream head-to-weir height ratio (h⁄P), the inlet-to-outlet width ratio (w_i⁄w_o), the key length-to-width ratio (L_cy⁄w_cy), and the number of keys (N) were considered as inputs, with Cd as the output. The results indicated that the minimum value of the R2 statistical index and the maximum value of the RMSE for the mentioned models during the validation phase were R2=0.99 and RMSE=0.01, respectively. The developed MLPNN model consisted of two hidden layers, with four neurons in the first layer and two neurons in the second layer, both utilizing the sigmoid tangent activation function. The SVM model employed a radial basis function (RBF) kernel. Sensitivity analysis of the models revealed that the most influential parameters in modeling and estimating the discharge coefficient were h_o⁄P.