Machine learning has been increasingly used in decoding brain states from functional magnetic resonance imaging (fMRI). One important application is to classify the levels of pain perception from patients' fMRI for clinical pain assessment. However, the huge number of fMRI features and the complex relationships between fMRI and pain levels affect the performance of pain classification models heavily. In this article, we introduce a new fuzzy-rule-based hybrid optimization approach for dimension reduction and multiclassification problems using chaotic map, crow search optimization (CSO), and self-organizing fuzzy logic prototype (SOFLP). The approach is named as CCSO-SOFLP. In the proposed approach, chaotic map-based CSO is employed to find the optimal features from ultra-high-dimensional fMRI, and the fuzzy-rule-based SOFLP is employed for multiclassification of pain levels. In this sense, CSO is provided to avoid being stuck in local minima and to increase the computational performance. On the other hand, multilayer SOFLP classifier can continuously learn from new data and identify prototypes from the observed data and use them to build fuzzy rules, to define a suitable local area for each prototype, and to avoid overlapping. The proposed approach is applied on a pain-evoked fMRI data set to classify the levels of pain. Results indicate that the proposed approach can decode levels of pain and identify predictive fMRI patterns with higher accuracy and convergence speed and shorter execution time. Therefore, the new type of fuzzy-rule-based system with chaotic swarm intelligence holds great potential to predict pain perception in clinical uses.