This study presents a predictive model designed to estimate the weight of native fish species using physiological parameters, offering a non-invasive and accurate approach to aquaculture management. The research demonstrates that advanced machine learning models, particularly Random Forest and Decision Tree algorithms, significantly outperform traditional linear models in predicting fish weight by analyzing a comprehensive dataset of morphometric and physiological measurements. These algorithms excel because they capture complex, non-linear relationships within the data, achieving superior accuracy with an \( R^2 \) value approaching 1.0. The findings underscore the potential of machine learning to revolutionize aquaculture practices, providing reliable, data-driven tools to enhance fish growth monitoring, optimize feeding strategies, and reduce handling stress on aquatic species. These findings validate our model’s effectiveness and underscore the potential of machine learning in revolutionizing aquaculture practices.