The existence of various sounds from different natural and unnatural sources in the deep sea has caused the classification and identification of marine mammals intending to identify different endangered species to become one of the topics of interest for researchers and activists field. In this paper, first, an experimental data set was created using a designed scenario. The Whale Optimization Algorithm (WOA) is then used to train the multilayer perceptron neural network (MLP-NN). However, due to the large size of the data, the algorithm has not determined a clear boundary between the exploration and extraction phases. Next, to support this shortcoming, the fuzzy inference is used as a new approach to developing and upgrading WOA called FWOA. Fuzzy inference By setting FWOA control parameters can well define the boundary between the two phases of exploration and extraction. To measure the performance of the designed categorizer, in addition to using it to categorize benchmark datasets, five benchmarking algorithms CVOA, WOA, ChOA, BWO, PGO, were also used for MLP-NN training. The measured criteria are concurrency speed, ability to avoid local optimization, and classification rate. The simulation results showed that FWOA has the highest classification accuracy in classifying both sets of marine mammal datasets and provides better results than the other five benchmark algorithms.