The objective of this study was to evaluate the main reproductive aspects of the marine cyclopoid copepod Cyclopina sp under laboratory conditions and to design, to implement and to validate a framework for the development of decision system support based on fuzzy set theory using clusters and dynamic tables. To validate the proposed framework, a fuzzy inference system was developed with the aim to estimate reproductive performance (Average fertility – AF, Reproductive frequency – RF, and Reproductive events number – REN) of the marine copepod cyclopina sp submitted to different thermal water and pH values conditions and compared with other Artificial Neural Networks models. The results show that the determination coefficients (R2) for the three output variables for the Fuzzy Inference System – FIS were 1.0, 1.0, and 0.999, respectively. The mean values of the standard deviations were 0.037 eggs, 0.061 hours, and 0.027 reproductive events, respectively, representing mean percentage errors of 0.240, 0.181, and 1.039 %, showing a better accuracy than the ANN-based one. The proposed framework may provide an effective means to draw a pattern to the development of fuzzy systems.