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Prediction of long-term rainfall patterns is a highly challenging task in the hydrological field due to random nature of rainfall events. The contribution of monthly rainfall is important in agriculture and hydrological tasks. This paper proposes two data-driven models, namely biogeography-based extreme learning machine (BBO-ELM) and deep neural network (DNN), to predict one, two, and three month-ahead rainfall over India (All-India and six other homogeneous regions). Three other data-driven models called ELM, genetic algorithm (GA)-based ELM, and particle swarm optimization (PSO)-based ELM are used to compare the performance of the proposed models. Firstly, partial autocorrelation function (PACF) is applied in all datasets to select the optimal number of lags for input to the models. Secondly, the wavelet-based data pre-processing technique is applied in selected optimal lags and feed to the proposed …