According to literature, there are two aspects of a successful approach for seasonal forecasting of tropical cyclones, including factors relating to the formation and operation of the storms and regression methods. Dealing with the factors, El Niño–Southern Oscillation, and other global factors such as Quasi-Biennial Oscillation, Pacific Decanal Oscillation, etc. and local factors such as sea surface temperature, sea level pressures, etc. were examined for tropical cyclone forecasting. For regression, the most previous works used the linear regression-based model for seasonal tropical forecasting. However, the seasonal tropical forecasting requires high-dimensional data, so the forecasting ability using linear regression will have drawback. In this work, we analyse literatures of forecasting factors and regression methods for tropical cyclone forecasting. A CF-ANFIS algorithm integrating a conjunct space cluster and Cascade-forward neural network are proposed to forecast the number of tropical cyclone making landfall. This algorithm resolves the drawback by considering all forecast factors with high-dimensional data. The experimental results indicated that the CF-ANFIS for seasonal forecast of tropical cyclones is a significantly effective approach with high accuracy in comparison with traditional ANFIS.