Forecasts are important in decision making and entail prediction of a future state of a
particular subject of interest. These forecasts depend heavily on historical data and the
assumption that the past behaviour of forecast inputs will replicate itself in the future. Current
linear and macroeconomic theory forecasting models used in Kenya lack reliable accuracy
when predictors are futuristic and subject to changes over time. Artificial Neural Network
(ANN) allow for the model to be more versatile in incorporating new predictors without
altering the structure of the model. They work exceptionally well in environments that are
nonlinear and where data is noisy and sometimes unavailable. The structure for the proposed
model is a Neural Network with Back Propagation learning algorithm incorporating rainfall
and M-Pesa use effects as additional inflation variables. The Backpropagation Neural Network
was selected as a useful alternative due to the non-linear data used and to facilitate forecasting
of future values. The adaptability of ANNs makes them most suitable for dynamic forecasting
and classification problems. The results obtained from the model indicated that the back
propagation was an appropriate algorithm that can be implemented in the process of inflation
forecasting. The forecasting was done based on inflation variables identified as true inputs to
the process of inflation forecasting. The model accuracy performance at 71.4286 % showed
that the model is reliable as a tool for inflation forecasting. The study found that the optimum
learning rate for the model was 0.5 while the momentum was at 0.9 for the training and 0.7 for
the testing and validation data. Total iterations varied between the train, test and validate
phases.