In this paper, the aim was to model and forecast the Victoria Falls Rainforest tourism demand using hierarchical forecasting methods. The methods are capable of capturing tourism data dynamics, producing precise, coherent and sensible short-term tourism forecasts. The monthly tourism data from the Zimbabwe Parks and Wildlife Management Authority (ZPWMA) is used. The data are disaggregated according to tourism source (international, regional and locals) and further disaggregated by age (adult and children). The ZPWMA needs future tourism forecasts for all tourism sources, adult and children tourists for planning purposes. Accurate and coherent forecasts for both adult and children tourists are needed for hotel accommodation including family rooms, child minders, recreational facilities, food types at the site to mention a few. Time series plots of the disaggregated tourism data indicated more foreign adult tourists and fewer children tourists at the site. To model and forecast the tourism demand, the top-down, bottom-up, and the optimal combination approaches are adopted. The exponential smoothing techniques (EST) and the autoregressive integrated moving average (ARIMA) methods are the forecasting methods considered under the mentioned approaches. Accuracy measures indicated the bottom-up approach under ARIMA models as the best approach to the data and produced sensible future tourism forecasts. An overall slow increase in future tourist arrivals is suggested by the models, with significant numbers expected from foreign adult tourists and less children tourists. Effective marketing strategies targeting the younger generation from abroad need to be implemented as this will impact positively to our economy in the near future. The educational issues around the rainforest have the potential to attract school tours from the rest of the world. The methods used are of significant benefit to the ZPWMA, tourism managers, investors, government and policy makers. These methods are strongly recommended as they give economically feasible solutions.