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In this paper, a novel approach for forecasting a time sequence of probability density functions is introduced, which is based on Functional Singular Spectrum Analysis (FSSA). This approach is designed to analyze functional time series and address the constraints in predicting density functions, such as non-negativity and unit integral properties. First, appropriate transformations are introduced to convert the time series of density functions into a functional time series. Then, FSSA is applied to forecast the new functional time series, and finally, the predicted functions are transformed back into the space of density functions using the inverse transformation. The proposed method is evaluated using real-world data, including the density of satellite imagery.