This paper presents a bio-inspired optimized classification model for assessing water quality. As fish gills histopathology is a good biomarker for indicating water pollution, the proposed classification model uses fish gills microscopic images in order to asses water pollution and determine water quality. The proposed model comprises five phases; namely, case representation for defining case attributes via pre-processing and feature extraction steps, retrieve, reuse/adapt, revise, and retain phases. Wavelet transform and edge detection algorithms have been utilized for feature extraction stage. Case-based reasoning (CBR) has been employed, along with the bio-inspired Gray Wolf Optimization (GWO) algorithm, for optimizing feature selection and the k case retrieval parameters in order to asses water pollution. The datasets used for conducted experiments in this research contain real sample microscopic images for fish gills exposed to copper and water in different histopathlogical stages. Experimental results showed that the average accuracy achieved by the proposed GWO-CBR classification model exceeded 97.2 % considering variety of water pollutants.