Understanding metabolic adaptations to seasonal fluctuations in ectothermic organisms is challenging, especially in tropical species where physiological responses are more pronounced than in temperate or polar counterparts. Traditional analytical methods often fail to account for the complex metabolic adjustments that are present in these substantial responses, and the high-dimensional characteristics of metabolomic data complicate the interpretation process when using conventional statistical methods. We utilized Non-negative Matrix Factorization (NMF), an unsupervised machine learning algorithm, to analyze monthly serum metabolomics data from tilapia over a year in order to overcome these limits. A deeper analysis using NMF demonstrated that carbohydrates gained prominence during warmer months, as evidenced by consistently elevated weights of glycolysis intermediates in our quantitative analysis. Furthermore, fatty acids remained an important factor in both warm and cold seasons. Amino acids emerged as particularly versatile metabolites, exhibiting adaptability during seasonal transitions. This flexibility suggests their crucial role in coordinating energy-related adaptations and potentially facilitating epigenetic and reproductive responses to changing environments. Serum aspartate composition during the warm-cold transition indicated sex-specific metabolic strategies, as sexual dimorphism was observed in the seasonal utilization of fatty acids and aspartate. Collectively, NMF objectively assesses the metabolic tactics of tropical fish and reveals latent patterns in real-world metabolic dynamics. Consequently, it possesses the potential to facilitate metabolomics-driven species conservation in response to environmental changes.