Inflation, defined as the trend of the continuous increasing of the general level of prices within a country’s economy during a time period, affects both private and public sector of the economies. Policy makers have the need to control and stabilize the rate of inflation at low levels to achieve economic growth and prosperity. Particularly, they use the rate of inflation as a measure to diagnose economic problems and then to apply the corresponding macroeconomic policies. So, inflation rate forecasting must be accurate and the measurement of inflation rate, which is usually depended on consumer price index (CPI), should be as accurate as possible. Although there are many different ways to anticipate the CPI, the most accurate methods are those that use artificial neural network models. Since WASD (weights and structure determination) neural networks have been demonstrated to address the drawbacks of traditional back-propagation neural networks, like poor training speed and local minimum, a three-layer metaheuristic fuzzy adaptive WASD neural network model, termed FCB-WASD, is taken into consideration. The FCB-WASD model performs better than other well-known machine learning techniques for predicting the CPI of countries.