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Wind-tunnel testing of C-shaped building model has been carried out to assess the pressure coefficient under varying conditions. This will provide practical knowledge for the estimation of design loads at various points on the structural frames and thus overcoming the inherent limitations of code and analytical procedures. This study will enable the designers to ascertain the position of critical locations on the surfaces of the model having maximum positive and negative mean pressure coefficients. Numerical analysis has also been carried out using Computational fluid dynamics (CFD) ANSYS Fluent. The results obtained by CFD have been found to compare well with the corresponding experimental results. This suggests the applicability of proposed technique for predicting pressures on building efficiently and accurately. Different correlations have also been developed using group method of data handling neural network, gene-expression programming, and multivariate adaptive regression spline (MARS) for the prediction of surface mean pressure coefficient. Among these model equations, MARS model equation has been proposed to predict the surface mean pressure coefficient more accurately.