Due to the lack of petroleum resources, stratigraphic reservoirs have become an important source of future discoveries. We describe a methodology for predicting reservoir sands from complex reservoir seismic data. Data analysis involves a bio-integrated framework called multi-modal machine learning fusion (MMMLF) based on neural networks. First, acoustic-related seismic attributes from post-stack seismic data were used to characterize the reservoirs. They enhanced the understanding of the structure and spatial distribution of petrophysical properties of lithostratigraphic reservoirs. The attributes were then classified as varied modal inputs into a central fusion engine for prediction. We applied the method to a dataset from Northeast China. Using seismic attributes and rock physics relationships as input data, MMMLF was performed to predict the spatial distribution of lithology in the Upper Guantao substrata. Despite the large scattering in the acoustic-related data properties, the proposed MMMLF methodology predicted the distribution of lithological properties through the gamma ray logs. Moreover, complex stratigraphic traps such as braided fluvial sandstones in the fluvio-deltaic deposits were delineated. These findings can have significant implications for future exploration and production in Northeast China and similar petroleum provinces around the world.